{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 基于父母购买行为预测孩子年龄的机器学习实现",
   "id": "53c6e617ecf3bcc9"
  },
  {
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-08-08T09:39:47.449809Z",
     "start_time": "2025-08-08T09:39:47.442831Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# https://tianchi.aliyun.com/dataset/45\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "from dateutil.relativedelta import relativedelta\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ],
   "id": "initial_id",
   "outputs": [],
   "execution_count": 122
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 1. 数据加载与预处理",
   "id": "baae40929ddeb4d8"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# 将日期增加十年，因为这个数据太久了，所以增加10年\n",
    "def add_10_years_to_birthday(data, col):\n",
    "    # 解析生日字符串为datetime对象\n",
    "    data[col] = pd.to_datetime(data[col], format='%Y%m%d', errors='coerce')\n",
    "\n",
    "    # 增加10年\n",
    "    data[col] += pd.DateOffset(years=10)\n",
    "\n",
    "    # 格式化回YYYYMMDD\n",
    "    data[col] = data[col].dt.strftime('%Y%m%d')\n",
    "    return data\n"
   ],
   "id": "c7d879917dca1bb0",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# 加载婴儿信息\n",
    "data_baby = pd.read_csv('../data/淘宝母婴购物数据集/(sample)sam_tianchi_mum_baby.csv')\n",
    "data_baby = add_10_years_to_birthday(data_baby, 'birthday')\n",
    "print(\"婴儿信息前5行：\")\n",
    "print(data_baby.head())\n",
    "print(\"\\n数据统计信息：\")\n",
    "print(data_baby.describe())\n"
   ],
   "id": "ea29a86161dd8d1b",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# 加载交易信息\n",
    "data_trade = pd.read_csv('../data/淘宝母婴购物数据集/(sample)sam_tianchi_mum_baby_trade_history.csv')\n",
    "data_trade = add_10_years_to_birthday(data_trade, 'day')\n",
    "print(\"交易信息前5行：\")\n",
    "print(data_trade.head())\n",
    "print(\"\\n数据统计信息：\")\n",
    "print(data_trade.describe())"
   ],
   "id": "720363746b6d217c",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "##  2. 数据清洗与特征工程",
   "id": "a3ab7585d802894e"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 2.1处理儿童信息数据",
   "id": "6e73f689feacb2fd"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# 计算孩子年龄，以月为单位\n",
    "def calculate_child_age(data):\n",
    "    # 解析生日字符串为datetime对象\n",
    "    # 使用 Pandas 库的 to_datetime() 函数将 DataFrame 中 'birthday' 列的数据转换为标准的 datetime 类型。\n",
    "    data['birthday'] = pd.to_datetime(data['birthday'], format='%Y%m%d', errors='coerce')\n",
    "\n",
    "    # 计算年龄，以月为单位\n",
    "    data['child_age_months'] = (pd.to_datetime('today') - data['birthday']).dt.days // 30\n",
    "\n",
    "    # 格式化回YYYYMMDD\n",
    "    data['birthday'] = data['birthday'].dt.strftime('%Y%m%d')\n",
    "    return data\n",
    "\n",
    "\n",
    "# 处理年龄数据\n",
    "data_baby = calculate_child_age(data_baby)\n",
    "# print(data_baby.head())\n",
    "# 处理性别信息\n",
    "data_baby['gender'] = data_baby['gender'].map({0: '女', 1: '男', 2: '未知'})\n",
    "print(data_baby.head())\n",
    "# 删除无效数据\n",
    "data_baby = data_baby[data_baby['gender'] != '未知']\n",
    "data_baby = data_baby.dropna(subset=['child_age_months'])\n",
    "data_baby = data_baby[data_baby['child_age_months'] > 0]  # 删除小于0的数据"
   ],
   "id": "dae8b0c90827d0f0",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# 输出性别列的唯一值\n",
    "# print(\"性别列的唯一值：\")\n",
    "# print(data_baby['gender'].unique())\n",
    "# print(\"处理后的数据统计信息：\")\n",
    "# data_baby.head()"
   ],
   "id": "53dc5beab3a43f38",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 2.2处理交易记录数据",
   "id": "ba87f3a160bb1a2d"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# 转换日期格式\n",
    "data_trade['day'] = pd.to_datetime(data_trade['day'], format='%Y%m%d', errors='coerce')\n",
    "\n",
    "# 计算交易金额（假设每个item_id代表一个商品，buy_mount表示购买数量）\n",
    "# 由于没有价格信息，我们只能使用购买数量作为近似\n",
    "data_trade['purchase_amount'] = data_trade['buy_mount']\n",
    "\n",
    "# 删除无效日期记录\n",
    "data_trade = data_trade.dropna(subset=['day'])\n",
    "\n",
    "# 提取年份和月份作为额外特征\n",
    "data_trade['purchase_year'] = data_trade['day'].dt.year\n",
    "data_trade['purchase_month'] = data_trade['day'].dt.month"
   ],
   "id": "29593865a820ac17",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# 查看所有列名\n",
    "# print(data_trade.columns)\n",
    "# print(type(data_trade))\n",
    "print(data_trade.head())"
   ],
   "id": "d90d5f5e29cd84ff",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 2.3 创建用户购买行为特征",
   "id": "65b9b477f65ba526"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "def create_user_purchase_features(data):\n",
    "    user_purchase_features = data.groupby('user_id').agg({\n",
    "        'auction_id': ['count', 'nunique'],  # 购买次数、购买商品数量\n",
    "        'buy_mount': ['sum', 'mean', 'max', 'min'],  # 购买金额、平均金额、最大金额、最小金额\n",
    "        'cat_id': 'nunique',  # 购买类别数量\n",
    "        'cat1': 'nunique',  # 购买的不同根类别 数量\n",
    "        'purchase_year': ['nunique', 'min', 'max'],  # 购买年份数量、最小年份、最大年份\n",
    "        'purchase_month': 'nunique'  # 购买月份数量\n",
    "    })\n",
    "\n",
    "    # 扁平化多级列索引\n",
    "    user_purchase_features.columns = ['_'.join(col).strip() for col in user_purchase_features.columns.values]\n",
    "\n",
    "    # 时间相关特征\n",
    "    latex_purchase = data.groupby('user_id')['day'].max().reset_index()\n",
    "    latex_purchase['days_since_last_purchase'] = (datetime.now() - latex_purchase['day']).dt.days\n",
    "    user_features = user_purchase_features.merge(latex_purchase[['user_id', 'days_since_last_purchase']],\n",
    "                                                 on='user_id')\n",
    "\n",
    "    # 用户购买频率特征\n",
    "    first_purchase = data.groupby('user_id')['day'].min().reset_index()\n",
    "    user_lifetime = (datetime.now() - first_purchase['day']).dt.days\n",
    "    user_features['purchase_freq'] = user_features['auction_id_count'] / (user_lifetime + 1)  # 避免除零\n",
    "\n",
    "    # 商品多样行特征\n",
    "    user_features['product_diversity'] = user_features['auction_id_nunique'] / (user_features['auction_id_count'] + 1)\n",
    "\n",
    "    return user_features\n",
    "\n",
    "\n",
    "user_features = create_user_purchase_features(data_trade)\n",
    "# print(user_features.head())\n"
   ],
   "id": "58c860ae1f36c08d",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "user_features.head()",
   "id": "e54c0c3ff81d6ac6",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 3. 数据合并与准备",
   "id": "5001089ea5dad185"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# 合并儿童信息和交易信息\n",
    "data_merged = data_baby.merge(user_features, on='user_id', how='inner')\n",
    "# 处理性别特征（独热编码）\n",
    "data_merged = pd.get_dummies(data_merged, columns=['gender'], drop_first=True)\n",
    "\n",
    "# 选择特征和目标变量\n",
    "features = [\n",
    "    'gender_男',\n",
    "    'auction_id_count',\n",
    "    'auction_id_nunique',\n",
    "    'buy_mount_sum',\n",
    "    'buy_mount_mean',\n",
    "    'buy_mount_max',\n",
    "    'buy_mount_min',\n",
    "    'cat_id_nunique',\n",
    "    'cat1_nunique',\n",
    "    'purchase_year_nunique',\n",
    "    'purchase_year_min',\n",
    "    'purchase_year_max',\n",
    "    'purchase_month_nunique',\n",
    "    'purchase_freq',\n",
    "    'product_diversity',\n",
    "    'days_since_last_purchase'\n",
    "]\n",
    "target = 'child_age_months'\n",
    "X = data_merged[features]\n",
    "y = data_merged[target]\n",
    "\n",
    "# 处理缺失值（用中位数填充）\n",
    "X = X.fillna(X.median())\n",
    "\n",
    "# 数据标准化\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ],
   "id": "1e670c1f0a3ecd8c",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 4. 模型训练与评估",
   "id": "9bd2f5215b2e5d0c"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "- Mean Absolute Error（MAE，平均绝对误差） 是机器学习和统计学中常用的评估指标，用于衡量预测值与真实值之间的平均绝对偏差。它直观地反映了模型预测的准确性，值越小表示模型性能越好。\n",
    "- R² Score（R-squared，决定系数） 是机器学习和统计学中用于评估回归模型性能的核心指标，它衡量的是模型对目标变量方差的解释能力。R² 的取值范围通常在 0 到 1 之间，值越接近 1 表示模型拟合效果越好。"
   ],
   "id": "975e958aac17aa1e"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-08T09:40:22.351248Z",
     "start_time": "2025-08-08T09:40:21.867843Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# 划分数据集和训练集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
    "# 初始化模型\n",
    "# model = LinearRegression()\n",
    "model = RandomForestRegressor(\n",
    "    n_estimators=200,\n",
    "    max_depth=15,\n",
    "    min_samples_split=5,\n",
    "    random_state=42,\n",
    "    n_jobs=-1\n",
    ")\n",
    "# 训练模型\n",
    "model.fit(X_train, y_train)\n",
    "# 预测结果\n",
    "y_pred = model.predict(X_test)\n",
    "# print('R-squared:', r2_score(y_test, y_pred))\n",
    "# 评估模型\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "# print('MAE:', mae)\n",
    "\n",
    "# 特征重要性\n",
    "importances  = model.feature_importances_\n",
    "feature_importance = pd.DataFrame({\n",
    "    'Feature': features,\n",
    "    'Importance': importances\n",
    "}).sort_values('Importance', ascending=False)"
   ],
   "id": "37748d68e38cdadb",
   "outputs": [],
   "execution_count": 124
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "print(\"\\n特征重要性排序:\")\n",
    "print(feature_importance)"
   ],
   "id": "27ef34f46ad8cec3",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# 可视化特征重要性\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.barplot(x='Importance', y='Feature', data=feature_importance)\n",
    "plt.title('Feature Importance')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "65aefe9a3fef275f",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 5. 模型优化与验证",
   "id": "e45670fd8517ed70"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-08T09:35:34.981554Z",
     "start_time": "2025-08-08T09:35:26.591205Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# 交叉验证\n",
    "cv_scores = cross_val_score(\n",
    "    model,\n",
    "    X_scaled,\n",
    "    y,\n",
    "    cv=5,\n",
    "    scoring='neg_mean_absolute_error'\n",
    ")\n",
    "print(f\"\\n交叉验证MAE: {-cv_scores.mean():.2f} ± {cv_scores.std():.2f} months\")\n",
    "\n",
    "# 预测结果可视化\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_pred, alpha=0.3)\n",
    "plt.plot([y.min(), y.max()], [y.min(), y.max()], 'r--')\n",
    "plt.xlabel('Actual Age (months)')\n",
    "plt.ylabel('Predicted Age (months)')\n",
    "plt.title('Actual vs Predicted Age')\n",
    "plt.show()"
   ],
   "id": "d081fd94c4980d98",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "交叉验证MAE: 21.14 ± 1.31 months\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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Xena2bt06AzAmTJhQde711183unXrZrhcLqNnz57Ghx9+aIwePdro1q3bLt//7LPPGn369DGSkpKMtLQ0o1evXsbNN99s5Obm7vE1RUTqE4th/G5egIiIiEgtOfTQQ8nKyuLLL79MdCkiIrVKa6RERERkn4XDYSKRSLVzs2fP5ueff2bw4MGJKUpEpA5pREpERET22bp16xg2bBgXXnghrVu3Zvny5UyfPh2Px8OSJUto3rx5oksUEalVajYhIiIi+6xZs2b06dOH559/noKCAlJSUhg1ahT333+/QpSINEoakRIREREREYmT1kiJiIiIiIjESUFKREREREQkTlojBcRiMXJzc0lLS8NisSS6HBERERERSRDDMCgrK6N169ZYrXsed1KQAnJzc/e4c7uIiIiIiDQ9GzdupG3btnt8XkEKSEtLA8wfVnp6eoKrERERERGRRPF6vbRr164qI+yJghRUTedLT09XkBIRERERkT9d8qNmEyIiIiIiInFSkBIREREREYmTgpSIiIiIiEicFKRERERERETipCAlIiIiIiISJwUpERERERGROClIiYiIiIiIxElBSkREREREJE4KUiIiIiIiInFSkBIREREREYmTgpSIiIiIiEicFKRERERERETipCAlIiIiIiISJwUpERERERGROClIiYiIiIiIxElBSkREREREJE4KUiIiIiIikjh+f6Ir2CsKUiIiIiIisv8ZBvzrX9ClC3zySaKriZuClIiIiIiI7H8TJ8L550NuLjz5ZKKriZuClIiIiIiI7H8XXQQpKXD33fDBB4muJm72RBcgIiIiIiKNnGHAa6/Bli1w883mucMPh02bICMjoaXtLQUpERERERGpO4sWwfjx8L//gcMBp54KXbuazzXQEAWa2iciIiIiInVh2zYzQPXpY4aoyml8HTsmurJaoREpERERERGpPbEYvPgi/PWvUFhonjvnHHj4YWjbNrG11SIFKRERERERqT1bt8INN4DPBz16wFNPwXHHJbqqWqcgJSIiIiIi+6a8HFJTza9btYL774dQCK67zlwX1QhpjZSIiIiIiOydaBSmT4cOHeDrr3ecHz/e3CeqkYYoUJASEREREZG98d130K8fXHMNFBfD//1foivarxSkRERERESk5vLz4bLLoH9/WLgQPB548kmYMSPRle1XWiMlIiIiIiI18+qrcP31UFJiHo8da66HatkykVUlhIKUiIiIiIjUjMtlhqjDDoOnnzZHpZooBSkREREREdm9vDxYsQIGDTKPzzoL7HY49VSw2RJbW4JpjZSIiIiIiFQXDsNjj8FBB5nhads287zFAmec0eRDFChIiYiIiIjIzmbPNqfuTZwIZWXQsSMUFSW6qnpHQUpERERERGDTJjjvPBgyBJYuhebN4bnnzDbnXbokurp6R2ukRERERESauoIC6NHDHIGyWuHqq+HuuyEzM9GV1VsKUiIiIiIiTV1WlrkWavlymDbNnNonf0hT+0REREREmpoNG8xpfGvW7Dj31FPw7bcKUTWkESkRERERkaYiEIBHHoF77wW/33y8/775XHJyQktraBSkRERERESagn//G66/fsco1MCB5joo2Sua2iciIiIi0pj99huccgqMGmWGqFatYMYMs815r16Jrq7BUpASEREREWnMZs6Ejz4Cux1uuslsKHH++ebmurLXNLVPRERERKQxMQwoKYFmzczjm26C1avh5pvNFudSKzQiJSIiIiLSWKxaBSNHwuDBEImY59xueOklhahapiAlIiIiItLQ+Xxw663Qsyd89hksWwbz5ye6qkZNQUpEREREpKEyDHj7bejeHe67D0IhGDECliyB/v0TXV2jltAg9Y9//IPevXuTnp5Oeno6/fv359NPP616fvDgwVgslmqPq6++uto9NmzYwKhRo0hOTiY7O5tJkyYRqRzGFBERERFprEpK4IQT4KyzYONG6NjR3BPq3/+Ggw5KcHGNX0KbTbRt25b777+fAw88EMMwePnllzn11FP56aefOPjggwG44ooruOuuu6q+J3mnjcKi0SijRo0iJyeHuXPnsmXLFi6++GIcDgf33Xfffn8/IiIiIiL7jcdjbrDrcsEtt8Bf/wpJSYmuqsmwGIZhJLqInWVmZvLQQw9x2WWXMXjwYA499FAef/zx3V776aefctJJJ5Gbm0vLli0BmD59OrfccgsFBQU4nc4avabX68Xj8VBaWkp6enptvRURERERkdpjGPDWW+bUvcrPrCtWmG3NO3dObG2NSE2zQb1ZIxWNRnn99dfx+Xz032k+54wZM2jRogU9e/Zk8uTJVFRUVD03b948evXqVRWiAIYPH47X62Xp0qV7fK1gMIjX6632EBERERGpt5YsgSFD4JxzYKfZWnTtqhCVIAnfR2rx4sX079+fQCBAamoq7733Hj22t2Y8//zz6dChA61bt+aXX37hlltuYcWKFbz77rsA5OXlVQtRQNVxXl7eHl9z6tSpTJkypY7ekYiIiIhILSkthTvvhKeegmjUnLrXokWiqxLqQZDq2rUrixYtorS0lLfffpsxY8YwZ84cevTowZVXXll1Xa9evWjVqhVDhw5lzZo1dN6H5D158mQmTpxYdez1emnXrt0+vQ8RERERkVpjGPDqq+Ymulu3mufOOAMefRQ6dEhsbQLUg6l9TqeTLl260KdPH6ZOncohhxzCE088sdtr+/XrB8Dq1asByMnJYWvlL9Z2lcc5OTl7fE2Xy1XVKbDyISIiIiJSb9xzD4wZY4aogw6Czz+Hd95RiKpHEh6kfi8WixEMBnf73KJFiwBo1aoVAP3792fx4sXk5+dXXfPll1+Snp5eNT1QRERERKTBufxyaNUK7r8fFi8225xLvZLQqX2TJ0/mxBNPpH379pSVlTFz5kxmz57N559/zpo1a5g5cyYjR46kefPm/PLLL0yYMIGBAwfSu3dvAE444QR69OjBRRddxIMPPkheXh633XYb48aNw+VyJfKtiYiIiIjUTCwGL74I8+fD9OnmuVatYO1as7W51EsJDVL5+flcfPHFbNmyBY/HQ+/evfn88885/vjj2bhxI//5z394/PHH8fl8tGvXjtGjR3PbbbdVfb/NZuPjjz/mmmuuoX///qSkpDBmzJhq+06JiIiIiNRbCxbAuHHwww/m8bnnwuDB5tcKUfVavdtHKhG0j5SIiIiI7FdFRfC3v8Fzz5mNJdLSzO58110HDkeiq2vSapoNEt61T0RERESkyYhGzfB0661QXGyeu/BCePBBczqfNBgKUiIiIiIi+0swCFOnmiGqVy94+mk49thEVyV7QUFKRERERKQuFRZCZiZYrZCcDNOmmY0krr0W7Po43lDVu/bnIiIiIiKNQiRihqYDD4QXXthx/uST4frrFaIaOAUpEREREZHa9t//Qp8+ZvOIkhJ4661EVyS1TEFKRERERKS2bNkCF11krnv65Rdo1gz+8Q/49NNEVya1TOOJIiIiIiK14c034fLLoawMLBa44gq4915o0SLRlUkdUJASEREREakNnTpBeTkceaS5NuqIIxJdkdQhTe0TEREREdkbmzaZo1CVjjgC5syBefMUopoABSkRERERkXiEQvDAA9Ctm7keatWqHc8de6zZ5lwaPU3tExERERGpqS++MDvxrVxpHh99tNnmXJocxWURERERkT+zfj2MHg3Dh5shqmVLePlls8159+6Jrk4SQCNSIiIiIiJ/xO+Hvn2hsBBsNhg/HqZMAY8n0ZVJAilIiYiIiIj8kaQkmDABPv/c7MbXq1eiK5J6QFP7RERERER29ttvcOqpZge+SjffDLNnK0RJFY1IiYiIiIiAOYXv/vvNjnzBIGzcCAsXmpvr2vWxWarTb4SIiIiING2GAR9+CH/5C6xbZ54bNgyeesoMUSK7oSAlIiIiIk3XqlVw/fXw2Wfmcbt28OijZoc+hSj5A1ojJSIiIiJN1/z5ZohyOGDyZFi2DM48UyFK/pRGpERERESk6TAM2LTJHHkCOO88WLwYLrkEDjoosbVJg6IRKRERERFpGpYtgxNOgCOOgNJS85zFAlOnKkRJ3BSkRERERKRxKysz25f37g3/+Q+UlMDcuYmuSho4BSkRERERaZwMA/71L+jWDR56CCIROPlk+PVXOPHERFcnDZzWSImIiIhI4xMKwfDh5ia6AJ07wxNPwKhRCS1LGg+NSImIiIhI4+N0QocOkJQE99wDS5YoREmtUpASERERkYYvFoNXXoG1a3ece/BBs8HErbeC25242qRRUpASERERkYZt0SI49lgYMwYmTNhxPjvbHJUSqQMKUiIiIiLSMG3bBuPHQ58+Zhe+lBTo398cnRKpY2o2ISIiIiINSywGL74If/0rFBaa5845Bx5+GNq2TWxt0mQoSImIiIhIw/Lss3DNNebXPXrAtGkwZEhia5ImR1P7RERERKT+M4wdX48ZY26u+8gj5voohShJAI1IiYiIiEj9FY3Cc8/Bu+/Cp5+CzWa2NP/pJ7BqTEASR799IiIiIlI/ffcdHHmkOY3vyy/h9dd3PKcQJQmm30ARERERqV/y8+HSS80OfD/+CB4PPPmk2VBCpJ7Q1D4RERERqR+iUXjmGfj736G01Dx3ySUwdSq0bJnY2kR+R0FKREREROoHiwVmzjRD1OGHw9NPw1FHJboqkd1SkBIRERGRxNmyBdLSIDXVXPf0zDPw/fdwxRVmYwmRekprpERERERk/wuH4dFHoWtXuPvuHecPOwyuvlohSuo9jUiJiIiIyP41axaMHw+//moez51rro9SeJIGRCNSIiIiIrJ/bNpkdt477jgzRLVoAc8/D3PmKERJg6MRKRERERGpex99BOedBz6fuRbqmmvMKX3NmiW6MpG9oiAlIiIiInXvsMPM/x0wAKZNg0MPTWg5IvtKU/tEREREpPatXw+PP77juG1b+OEH+PZbhShpFBSkRERERKT2BAJwzz3QvTtMmABffbXjuR49zL2iRBoBTe0TERERkdrxySdwww2wZo15PGgQtGqV2JpE6khCR6T+8Y9/0Lt3b9LT00lPT6d///58+umnVc8HAgHGjRtH8+bNSU1NZfTo0WzdurXaPTZs2MCoUaNITk4mOzubSZMmEYlE9vdbEREREWm6fvsNTjkFTjrJDFGtW8PMmWab8x49El2dSJ1IaJBq27Yt999/PwsXLmTBggUcd9xxnHrqqSxduhSACRMm8NFHH/HWW28xZ84ccnNzOeOMM6q+PxqNMmrUKEKhEHPnzuXll1/mpZde4vbbb0/UWxIRERFpWmIxGDHC7Mpnt8OkSbB8udmhT9P4pBGzGIZhJLqInWVmZvLQQw9x5plnkpWVxcyZMznzzDMBWL58Od27d2fevHkcddRRfPrpp5x00knk5ubSsmVLAKZPn84tt9xCQUEBTqdzt68RDAYJBoNVx16vl3bt2lFaWkp6enrdv0kRERGRhqzy42NlUHrzTXjuOXjqKejWLXF1idQCr9eLx+P502xQb5pNRKNRXn/9dXw+H/3792fhwoWEw2GGDRtWdU23bt1o37498+bNA2DevHn06tWrKkQBDB8+HK/XWzWqtTtTp07F4/FUPdq1a1d3b0xERESkMVm1CkaOhBdf3HHurLPgiy8UoqRJSXiQWrx4MampqbhcLq6++mree+89evToQV5eHk6nk4yMjGrXt2zZkry8PADy8vKqhajK5yuf25PJkydTWlpa9di4cWPtvikRERGRxsbng7/9DXr2hM8+gzvugHDYfM5i0TQ+aXIS3rWva9euLFq0iNLSUt5++23GjBnDnDlz6vQ1XS4XLperTl9DREREpFEwDHj7bZg4ETZtMs+NGAFPPgkOR2JrE0mghAcpp9NJly5dAOjTpw/z58/niSee4JxzziEUClFSUlJtVGrr1q3k5OQAkJOTww8//FDtfpVd/SqvEREREZG9tHIlXHvtjr2gOnY0N9k95RSNQEmTl/Cpfb8Xi8UIBoP06dMHh8PBVztt4rZixQo2bNhA//79Aejfvz+LFy8mPz+/6povv/yS9PR0eqjVpoiIiMi+KS42Q5TLZU7l+/VXOPVUhSgREjwiNXnyZE488UTat29PWVkZM2fOZPbs2Xz++ed4PB4uu+wyJk6cSGZmJunp6Vx33XX079+fo446CoATTjiBHj16cNFFF/Hggw+Sl5fHbbfdxrhx4zR1T0RERCRehgFLl5rroACOOgqeftqcynfAAYmtTaSeSWiQys/P5+KLL2bLli14PB569+7N559/zvHHHw/AY489htVqZfTo0QSDQYYPH84zzzxT9f02m42PP/6Ya665hv79+5OSksKYMWO46667EvWWRERERBqmJUtg/Hj47jszTHXubJ6/9trE1iVST9W7faQSoaa94kVEREQandJSc9retGkQjUJSErz8stnSXKQJanD7SImIiIjIfhSLmYHpoIPgiSfMEDV6NCxbphAlUgMJ79onIiIiIvuZYcDw4fCf/5jHXbua7cxPOCGxdYk0IBqREhEREWlqLBYYOBBSUuD+++GXXxSiROKkNVJojZSIiIg0crEY/POf0KMHHH20eS4QgMJCaNs2sbWJ1DM1zQaa2iciIiLSmM2fD+PGmf/buzcsXAh2O7jdClEi+0BT+0REREQao8JCuPJK6NfPDFFpaTBmTKKrEmk0NCIlIiIi0phEo/Dss3DrrbBtm3nuwgvhwQehVavE1ibSiChIiYiIiDQmH320YxPd3r3N/aGOPTaxNYk0QgpSIiIiIg1dLAbW7Ss2Tj0VTj7Z7MJ39dXmeigRqXVaIyUiIiLSUEUi8NRT0LMneL3mOYsFPvwQxo9XiBKpQwpSIiIiIg3Rt99Cnz5w/fWwbJm5LkpE9hsFKREREZGGZMsWs3nEwIHmRrqZmTB9OkyYkOjKRJoUBSkRERGRhsAw4NFH4aCDYMYMcwrfVVfBypXm/9psia5QpEnRxFkRERGRhsBigR9/hPJyc2+oadOgb99EVyXSZClIiYiIiNRXmzaZAapNG/P4oYfguONg7NgdXfpEJCH0f4EiIiIi9U0oBPffD127ms0kKrVqBZdeqhAlUg9oREpERESkPvniC7juOnPtE8DWreDzQUpKYusSkWr05wwRERGR+mD9ehg9GoYPN0NUy5bwyitmm3OFKJF6RyNSIiIiIok2ezaMHAl+v9l977rr4M47weNJdGUisgcKUiIiIiKJduSRkJUFnTqZ3fh69kx0RSLyJzS1T0RERGR/++03uOkmiEbN4+Rk+N//YNYshSiRBkJBSkRERGR/8fvhjjugRw945BH45z93PNe2rdnqXEQaBE3tExEREalrhgEffAATJsC6dea5oUPhmGMSWpaI7D0FKREREZG6tHIl3HADfPaZedyuHTz6qNmhTyNQIg2WgpSIiIhIXbr8crOFudMJkybB5MlqZy7SCGiNlIiIiEhtMgwIh3ccP/oojBoFS5bAPfcoRIk0EgpSIiIiIrVl2TI4/nizoUSlvn3h44/hwAMTV5eI1DoFKREREZF9VVZmTtvr3Ru++gqefhq83kRXJSJ1SEFKREREZG8ZBsycCV27wsMPQyQCp5wCP/0E6emJrk5E6pCaTYiIiIjsjdWr4bLL4JtvzOPOneHJJ2HkyMTWJSL7hUakRERERPaGywULFkBSktlEYskShSiRJkQjUiIiIiI1EYuZo0+DB5vH7drBa6/B4YdDhw4JLU1E9j+NSImIiIj8mZ9+gmOOgSFDYM6cHedPP10hSqSJUpASERER2ZPiYhg3zmxhPm+euQfU+vWJrkpE6gEFKREREZHfi8Xg+efNbnzPPGMen3MOLF8OF1+c6OpEpB6Ia41USUkJ7733Ht9++y3r16+noqKCrKwsDjvsMIYPH87RRx9dV3WKiIiI7D9nngnvvWd+3aMHTJtmTusTEdmuRiNSubm5XH755bRq1Yp77rkHv9/PoYceytChQ2nbti2zZs3i+OOPp0ePHrzxxht1XbOIiIhI3TrrLEhLg0cfhUWLFKJEZBc1GpE67LDDGDNmDAsXLqRHjx67vcbv9/P+++/z+OOPs3HjRm666aZaLVRERESkTkSj8Oyz0Lw5nH22ee7cc2HYMMjKSmxtIlJvWQzDMP7soqKiIpo3b17jm8Z7faJ5vV48Hg+lpaWkaxdyERGRpmPePLOZxE8/QcuWsGIFeDyJrkpEEqim2aBGU/viDUUNKUSJiIhIE7R1K1xyCRx9tBmiPB647TazK5+ISA3E3bXv5Zdf5pNPPqk6vvnmm8nIyODoo49mvdqBioiISH0WicCTT5rd+F56yTx36aWwciWMHw/2uPpwiUgTFneQuu+++0hKSgJg3rx5PP300zz44IO0aNGCCRMm1HqBIiIiIrXmp5/ghhugtBQOP9yc2vfCC5CdnejKRKSBifvPLhs3bqRLly4AvP/++4wePZorr7ySAQMGMHjw4NquT0RERGTfBALgdptfH3EETJhgjkhdfjnYbImtTUQarLhHpFJTUykqKgLgiy++4PjjjwfA7Xbj9/trtzoRERGRvRUOwyOPQIcOsHbtjvOPPgpXXaUQJSL7JO4gdfzxx3P55Zdz+eWXs3LlSkaOHAnA0qVL6dixY1z3mjp1KkcccQRpaWlkZ2dz2mmnsWLFimrXDB48GIvFUu1x9dVXV7tmw4YNjBo1iuTkZLKzs5k0aRKRSCTetyYiIiKNxddfwyGHwE03QX4+TJ+e6IpEpJGJO0g9/fTT9O/fn4KCAt55552qDn0LFy7kvPPOi+tec+bMYdy4cXz33Xd8+eWXhMNhTjjhBHw+X7XrrrjiCrZs2VL1ePDBB6uei0ajjBo1ilAoxNy5c3n55Zd56aWXuP322+N9ayIiItLQbdoE55wDQ4fCsmXQooW5Bmrq1ERXJiKNTI32kdpfCgoKyM7OZs6cOQwcOBAwR6QOPfRQHn/88d1+z6effspJJ51Ebm4uLVu2BGD69OnccsstFBQU4HQ6//R1tY+UiIhII/D443DrrVBRAVYrXHst3HUXNGuW6MpEpAGpaTbYqx6fJSUl/PDDD+Tn5xOLxarOWywWLrroor25JQClpaUAZGZmVjs/Y8YMXnvtNXJycjj55JP5+9//TnJyMmB2DuzVq1dViAIYPnw411xzDUuXLuWwww7b5XWCwSDBYLDq2Ov17nXNIiIiUk+UlJghasAAmDYNDj000RWJSCMWd5D66KOPuOCCCygvLyc9PR2LxVL13L4EqVgsxl/+8hcGDBhAz549q86ff/75dOjQgdatW/PLL79wyy23sGLFCt59910A8vLyqoUooOo4Ly9vt681depUpkyZsld1ioiISD2xfj2UlUHl54ZbboFu3cypfTt9PhERqQtxB6kbb7yRSy+9lPvuu69qVKg2jBs3jiVLlvDf//632vkrr7yy6utevXrRqlUrhg4dypo1a+jcufNevdbkyZOZOHFi1bHX66Vdu3Z7V7iIiIjsX4EAPPSQue6pe3f44QezA19SEpx7bqKrE5EmIu5mE5s3b+b666+v1RA1fvx4Pv74Y2bNmkXbtm3/8Np+/foBsHr1agBycnLYunVrtWsqj3NycnZ7D5fLRXp6erWHiIiINACffGKOQN1+O/j9kJYGxcWJrkpEmqC4g9Tw4cNZsGBBrby4YRiMHz+e9957j6+//ppOnTr96fcsWrQIgFatWgHQv39/Fi9eTH5+ftU1X375Jenp6fTo0aNW6hQREZEEW7MGTj4ZTjrJ/Lp1a/jXv2DWLMjKSnR1ItIE1Whq34cfflj19ahRo5g0aRK//vorvXr1wuFwVLv2lFNOqfGLjxs3jpkzZ/LBBx+QlpZWtabJ4/GQlJTEmjVrmDlzJiNHjqR58+b88ssvTJgwgYEDB9K7d28ATjjhBHr06MFFF13Egw8+SF5eHrfddhvjxo3D5XLVuBYRERGppxYtgqOOgmAQ7HaYOBFuu80cjRIRSZAatT+3Wms2cGWxWIhGozV/8T0sBH3xxRcZO3YsGzdu5MILL2TJkiX4fD7atWvH6aefzm233VZtOt769eu55pprmD17NikpKYwZM4b7778fu71mS8DU/lxERKQei8XgmGMgJQWeespsKCEiUkdqmg3q1T5SiaIgJSIiUo+sXAn33APPPAOpqea5khLweNSNT0TqXE2zQdxrpF555ZVqezBVCoVCvPLKK/HeTkRERMTk88HkyWYziVdfNcNUpYwMhSgRqVfiDlKXXHJJ1ca5OysrK+OSSy6plaJERESkCTEMePNNc8re/fdDOAwnngiXXZboykRE9ijufaQMw9jt2qZNmzbh8XhqpSgRERFpIn79Fa67Dr7+2jzu2BGeeMLs0KcRKBGpx2ocpA477DAsFgsWi4WhQ4dWa+QQjUZZu3YtI0aMqJMiRUREpJG6914zRLlc8Ne/wi23mBvriojUczUOUqeddhpg7uM0fPhwUisXfwJOp5OOHTsyevToWi9QREREGhHDgIoKswMfwIMPQjQK990HBxyQ2NpEROIQd9e+l19+mXPOOQe3211XNe136tonIiKyHyxeDOPHQ04OvPFGoqsREdmtmmaDuNdIjRkzBjC79OXn5xOLxao93759+3hvKSIiIo1ZSQnccQc8/bQ5+pSUBBs3Qrt2ia5MRGSvxR2kVq1axaWXXsrcuXOrna9sQhHPhrwiIiLSiMViZhvzm2+G/Hzz3OjR8OijClEi0uDFHaTGjh2L3W7n448/plWrVrvt4CciIiJN3Pr1cN55MG+eedy1Kzz1FBx/fGLrEhGpJXEHqUWLFrFw4UK6detWF/WIiIhIY9CihTl9LyUFbr8d/vIXcDoTXZWISK2JO0j16NGDwsLCuqhFREREGqpYDN57D04/HaxWM0C98QZ06ABt2iS6OhGRWmeN9xseeOABbr75ZmbPnk1RURFer7faQ0RERJqY+fPhqKPgzDPhpZd2nD/6aIUoEWm04h6RGjZsGABDhw6tdl7NJkRERJqYwkL429/g+efN/aHS0syRKRGRJiDuIDVr1qy6qENEREQaimgUnn0Wbr0Vtm0zz110kbm5bk5OYmsTEdlP4g5SgwYNqos6REREpKG4/PIdU/gOOQSmTYNjjkloSSIi+1vcQQqgpKSEF154gWXLlgFw8MEHc+mll+LxeGq1OBEREamHrr4aPvwQ7roLrroK7Hv1cUJEpEGzGIZhxPMNCxYsYPjw4SQlJXHkkUcCMH/+fPx+P1988QWHH354nRRal7xeLx6Ph9LSUtLT0xNdjoiISP0RicAzz0AgYG6sW8nnMzvziYg0MjXNBnEHqWOPPZYuXbrw3HPPYd/+F6hIJMLll1/Ob7/9xjfffLNvlSeAgpSIiMhufPMNjB8PixeDywXLl0PHjomuSkSkTtU0G8Q9Fr9gwYJqIQrAbrdz880307dv372rVkREROqP3Fxz9GnGDPM4MxPuuw/atUtsXSIi9Ujc+0ilp6ezYcOGXc5v3LiRtLS0WilKREREEiAchkcega5dzRBlsZhroFauNP/XZkt0hSIi9UbcQeqcc87hsssu44033mDjxo1s3LiR119/ncsvv5zzzjuvLmoUERGR/WHLFrjtNigvh3794IcfYPp0aN480ZWJiNQ7cU/te/jhh7FYLFx88cVEIhEAHA4H11xzDffff3+tFygiIiJ1qKQEMjLMr9u3hwcegNRUGDsWrHH/vVVEpMmIu9lEpYqKCtasWQNA586dSU5OrtXC9ic1mxARkSYnGITHHoN774VPP9U+UCIi29VZs4lKycnJ9OrVa2+/XURERBLl88/h+uvNtU8Ar7yiICUiEqe4g1QgEOCpp55i1qxZ5OfnE4vFqj3/448/1lpxIiIiUovWrYOJE+G998zjli3hoYfgwgsTWpaISEMUd5C67LLL+OKLLzjzzDM58sgjsVgsdVGXiIiI1KZp02DSJHNjXZvNHJG64w7weBJdmYhIgxR3kPr444/597//zYABA+qiHhEREakLGRlmiBo82AxVBx+c6IpERBq0uINUmzZttF+UiIhIfbdmDWzYAEOGmMcXXAAtWsDw4eb+UCIisk/i7mv6yCOPcMstt7B+/fq6qEdERET2RUUF3H67OeJ0/vng9ZrnLRYYMUIhSkSklsQ9ItW3b18CgQAHHHAAycnJOByOas8XFxfXWnEiIiJSQ4YB778PEyZA5R87e/Y0g5S29hARqXVxB6nzzjuPzZs3c99999GyZUs1mxAREUm0lSvhuuvgiy/M43btzD2izjhDI1AiInUk7iA1d+5c5s2bxyGHHFIX9YiIiEg8Nm6EXr0gFAKn0+zMN3kypKQkujIRkUYt7iDVrVs3/H5/XdQiIiIi8WrXDs46C4qL4Ykn4MADE12RiEiTEHezifvvv58bb7yR2bNnU1RUhNfrrfYQERGROvTrr3DSSTvWQQE8/zx88olClIjIfhT3iNSIESMAGDp0aLXzhmFgsViIRqO1U5mIiIjs4PXClCnw5JMQiUBaGvzrX+ZzbndiaxMRaYLiDlKzZs2qizpERERkdwwDZs401z5t2WKeO/VUuO++xNYlItLExR2kBg0aVBd1iIiIyO8tXgzjx8M335jHXbqYI1InnpjYukREpGZrpDZs2BDXTTdv3rxXxYiIiMhO3nzTDFFJSXDvvbBkiUKUiEg9UaMgdcQRR3DVVVcxf/78PV5TWlrKc889R8+ePXnnnXdqrUAREZEmIxaDgoIdx5Mnw9VXw/Ll8Le/gcuVuNpERKSaGk3t+/XXX7n33ns5/vjjcbvd9OnTh9atW+N2u9m2bRu//vorS5cu5fDDD+fBBx9k5MiRdV23iIhI4/Ljj+Y0vnAYvvsObDZIToZ//CPRlYmIyG5YDMMwanqx3+/nk08+4b///S/r16/H7/fTokULDjvsMIYPH07Pnj3rstY64/V68Xg8lJaWkp6enuhyRESkKSkuhttug+nTzcYSKSkwdy707p3oykREmqSaZoO4glRjpSAlIiL7XSwGL7xgTt8rKjLPnXcePPQQtGmT2NpERJqwmmaDuLv2iYiIyD7Kzzc31a1ce3zwwTBtGgwenNCyRESk5mrUbEJERERqUYsWYLFAejo89hj89JNClIhIA6MRKRERkboWjcJLL8G555proKxWeOUV8HggJyfR1YmIyF5I6IjU1KlTOeKII0hLSyM7O5vTTjuNFStWVLsmEAgwbtw4mjdvTmpqKqNHj2br1q3VrtmwYQOjRo0iOTmZ7OxsJk2aRCQS2Z9vRUREZPfmzYMjjoDLLzf3gqrUtatClIhIA5bQIDVnzhzGjRvHd999x5dffkk4HOaEE07A5/NVXTNhwgQ++ugj3nrrLebMmUNubi5nnHFG1fPRaJRRo0YRCoWYO3cuL7/8Mi+99BK33357It6SiIiIaetWGDsWjj7anLqXkQEdOya4KBERqS171bXv1VdfZfr06axdu5Z58+bRoUMHHn/8cTp16sSpp56618UUFBSQnZ3NnDlzGDhwIKWlpWRlZTFz5kzOPPNMAJYvX0737t2ZN28eRx11FJ9++iknnXQSubm5tGzZEoDp06dzyy23UFBQgNPp/NPXVdc+ERGpNZEIPPMM3H47lJaa5y67DKZOhaysxNYmIiJ/qqbZIO4RqX/84x9MnDiRkSNHUlJSQjQaBSAjI4PHH398rwsGKN3+H5zMzEwAFi5cSDgcZtiwYVXXdOvWjfbt2zNv3jwA5s2bR69evapCFMDw4cPxer0sXbp0t68TDAbxer3VHiIiIrVi8mS44QYzRPXpY26u+/zzClEiIo1M3EHqqaee4rnnnuPWW2/FZrNVne/bty+LFy/e60JisRh/+ctfGDBgQNXGvnl5eTidTjIyMqpd27JlS/Ly8qqu2TlEVT5f+dzuTJ06FY/HU/Vo167dXtctIiJSzfXXQ7t25ga7338P/foluiIREakDcQeptWvXcthhh+1y3uVyVVvbFK9x48axZMkSXn/99b2+R01NnjyZ0tLSqsfGjRvr/DVFRKQRCofhkUfgqqt2nGvXDn77zTy30x8cRUSkcYk7SHXq1IlFixbtcv6zzz6je/fue1XE+PHj+fjjj5k1axZt27atOp+Tk0MoFKKkpKTa9Vu3biVne6ejnJycXbr4VR7n7KEbksvlIj09vdpDREQkLl99BYccAjfdBM8+a44+VbJrdxERkcYu7iA1ceJExo0bxxtvvIFhGPzwww/ce++9TJ48mZtvvjmuexmGwfjx43nvvff4+uuv6dSpU7Xn+/Tpg8Ph4Kuvvqo6t2LFCjZs2ED//v0B6N+/P4sXLyY/P7/qmi+//JL09HR69OgR79sTERH5Yxs3wtlnw7BhsGyZufbpn/80W5yLiEiTsVdd+2bMmMGdd97JmjVrAGjdujVTpkzhsssui+s+1157LTNnzuSDDz6ga9euVec9Hg9JSUkAXHPNNfz73//mpZdeIj09neuuuw6AuXPnAmb780MPPZTWrVvz4IMPkpeXx0UXXcTll1/OfffdV6M61LVPRET+VDAIjz0Gd98NFRXmprrXXgt33QXNmiW6OhERqSU1zQZ7FaQqVVRUUF5eTnZ29l59v8Vi2e35F198kbFjxwLmhrw33ngj//rXvwgGgwwfPpxnnnmm2rS99evXc8011zB79mxSUlIYM2YM999/P/YaTq1QkBIRkT9VVgbdukFuLhxzDEybZk7tExGRRmW/BKnGQkFKRER2a9MmaN3aHH0C+OADM1BdcAHs4Y+BIiLSsNU0G8S9Gvawww7b7UiSxWLB7XbTpUsXxo4dy5AhQ+K9tYiISP0QCMCDD5qb6E6fDmPGmOf3YdN5ERFpXOJuNjFixAh+++03UlJSGDJkCEOGDCE1NZU1a9ZwxBFHsGXLFoYNG8YHH3xQF/WKiIjUrY8/hoMPhjvuMAPVp58muiIREamH4h6RKiws5MYbb+Tvf/97tfP33HMP69ev54svvuCOO+7g7rvv5lT95U5ERBqKNWvghhvgk0/M4zZtzD2izj47sXWJiEi9FPcaKY/Hw8KFC+nSpUu186tXr6ZPnz6UlpayfPlyjjjiCMrKymq12LqiNVIiIk3cc8/B+PEQCoHDARMnwm23QWpqoisTEZH9rKbZIO6pfW63u6r1+M7mzp2L2+0GIBaLVX0tIiJS7x18sBmijj8eFi+G++9XiBIRkT8U99S+6667jquvvpqFCxdyxPbNB+fPn8/zzz/P3/72NwA+//xzDj300FotVEREpNasWAE//gjnnWceH300zJ8PffqoG5+IiNTIXm/IO23aNFasWAFA165due666zj//PMB8Pv9VV38GgJN7RMRaSLKy+Gee+DRR8Fmg19/hU6dEl2ViIjUI3XW/hzgggsu4IILLtjlfDQaxWazkZSUtDe3FRERqRuGAW+9BTfeaO4NBeY0PmvcM9xFRESAvVgjtTsrV67klltuoW3btrVxOxERkdrz668wbBicc44Zojp1gg8/NNucd+iQ6OpERKSB2qsRKYCKigreeOMN/vnPfzJv3jz69u3LxIkTa7M2ERGRfeP1wlFHQVkZuN0weTJMmgSaOSEiIvso7iD13Xff8fzzz/PWW2/Rvn17li1bxqxZszj22GProj4REZH4GMaOhhHp6WYr80WL4LHHtB5KRERqTY2n9j3yyCMcfPDBnHnmmTRr1oxvvvmGxYsXY7FYaN68eV3WKCIiUjO//AJDhsC8eTvO3X47vP++QpSIiNSqGgepW265hdNOO43169fz0EMPccghh9RlXSIiIjVXUgI33ACHHw5z5sDNN+94Tg0lRESkDtT4vy533303b731Fp06deKWW25hyZIldVmXiIjIn4vF4KWXoGtXePJJiEbhzDNhxoxEVyYiIo1cjYPU5MmTWblyJa+++ip5eXn069ePQw45BMMw2LZtW13WKCIisquffoJjjoFLLoH8fOjWDb74wmxz3r59oqsTEZFGLu75DoMGDeLll18mLy+Pa6+9lj59+jBo0CCOPvpoHn300bqoUUREZFc//2yuhUpNhYceMo+PPz7RVYmISBNhMQzD2NebLF68mBdeeIGZM2eSn59fG3XtVzXdvVhERBIoFoO1a6Fz5x3Hd90FV1wBbdoktjYREWk0apoNaiVIVQqHwzgcjtq63X6jICUiUs/98AOMHw+bN8Py5ZCWluiKRESkkappNqjVVkYNMUSJiEg9VlBgjjgddRTMnw/l5eaeUCIiIgmmnrAiIlL/RKPwzDNmN77nnzc32b34YlixArQBvIiI1AP2RBcgIiJSjc9nhqWffjKPDzkEnn4aBgxIbF0iIiI70YiUiIjULykp5khURgZMmwYLFihEiYhIvVOjESmv11vjG6pZg4iIxCUSMUeczjgD2rUzzz3+OFitkJWV0NJERET2pEZBKiMjA4vFUqMbRqPRfSpIRESakDlzzG58S5bA3Lnwxhvm+ZYtE1uXiIjIn6hRkJo1a1bV1+vWreOvf/0rY8eOpX///gDMmzePl19+malTp9ZNlSIi0rjk5sJNN8G//mUeZ2bC0KFmU4ka/uFOREQkkeLeR2ro0KFcfvnlnHfeedXOz5w5k2effZbZs2fXZn37hfaREhHZT8JheOIJmDLFbGVuscBVV8E990Dz5omuTkREpO72kZo3bx59+/bd5Xzfvn354Ycf4r2diIg0JU88AZMmmSGqXz9zb6h//EMhSkREGpy4g1S7du147rnndjn//PPP065ykbCIiEilnSc+XHMN9OkD//ynuSaqT5/E1SUiIrIP4t5H6rHHHmP06NF8+umn9OvXD4AffviBVatW8c4779R6gSIi0kAFg/Doo/Cf/8CXX5pd+FJSzFEorYMSEZEGLu4RqZEjR7Jy5UpOPvlkiouLKS4u5uSTT2blypWMHDmyLmoUEZGG5rPPoFcv+Nvf4Ouv4cMPdzynECUiIo1A3M0mGiM1mxARqSXr1sGECfD+++ZxTg489BBccIEClIiINAh11mwC4Ntvv+XCCy/k6KOPZvPmzQC8+uqr/Pe//927akVEpGELheCuu6B7dzNE2WwwcSKsWAEXXqgQJSIijU7cQeqdd95h+PDhJCUl8eOPPxIMBgEoLS3lvvvuq/UCRUSkAbDZzOl7gQAMGQI//wyPPAIa5RcRkUYq7iB1zz33MH36dJ577jkcDkfV+QEDBvDjjz/WanEiIlKPrVkDFRXm1zYbTJ8Or78OX30FBx+c2NpERETqWNxBasWKFQwcOHCX8x6Ph5KSktqoSURE6rOKCvj736FHD5g6dcf5vn3hnHM0jU9ERJqEuINUTk4Oq1ev3uX8f//7Xw444IBaKUpEROohw4B33zXXQd1zj7ku6uefq+8TJSIi0kTEHaSuuOIKbrjhBr7//nssFgu5ubnMmDGDm266iWuuuaYuahQRkURbsQJGjIDRo2HDBmjfHt55Bz74QCNQIiLSJMW9Ie9f//pXYrEYQ4cOpaKigoEDB+Jyubjpppu47rrr6qJGERFJpJkzYexYCIfB6YSbb4bJkyE5OdGViYiIJMxe7yMVCoVYvXo15eXl9OjRg9TU1Nqubb/RPlIiIn9g/Xro1g2OOw6eeAK6dEl0RSIiInWmzvaRuvTSSykrK8PpdNKjRw+OPPJIUlNT8fl8XHrppftUtIiI1AO//goPP7zjuEMHWLwYPv5YIUpERGS7uEekbDYbW7ZsITs7u9r5wsJCcnJyiEQitVrg/qARKRERwOuFKVPgySchEoFvvoFjj010VSIiIvtVTbNBjddIeb1eDMPAMAzKyspwu91Vz0WjUf7973/vEq5ERKQBMAyYMQMmTYK8PPPcaadBu3YJLUtERKQ+q3GQysjIwGKxYLFYOOigg3Z53mKxMGXKlFotTkRE6tgvv8D48fDtt+Zxly7w1FNmhz4RERHZoxoHqVmzZmEYBscddxzvvPMOmZmZVc85nU46dOhA69at66RIERGpA+EwjBoFmzaZHfhuuw0mTgSXK9GViYiI1Hs1bjYxaNAgBg8ezNq1aznttNMYNGhQ1aN///57FaK++eYbTj75ZFq3bo3FYuH999+v9vzYsWOrRsEqHyN+91fS4uJiLrjgAtLT08nIyOCyyy6jvLw87lpERJqEWGzHBroOB0ydCmeeCcuWmS3NFaJERERqJO6ufV9//TVvv/32LuffeustXn755bju5fP5OOSQQ3j66af3eM2IESPYsmVL1eNf//pXtecvuOACli5dypdffsnHH3/MN998w5VXXhlXHSIiTcKPP8KAAeZ6qEoXXghvvWVusCsiIiI1FveGvFOnTuX//u//djmfnZ3NlVdeyZgxY2p8rxNPPJETTzzxD69xuVzk5OTs9rlly5bx2WefMX/+fPr27QvAU089xciRI3n44Yc11VBEBKC4GG69Ff7v/8zRqKIiOP98sMb9tzQRERHZLu7/im7YsIFOnTrtcr5Dhw5s2LChVora2ezZs8nOzqZr165cc801FBUVVT03b948MjIyqkIUwLBhw7BarXz//fd7vGcwGMTr9VZ7iIg0OtEoPPssHHQQTJ9uhqjzzoNZsxSiRERE9lHc/yXNzs7ml19+2eX8zz//TPPmzWulqEojRozglVde4auvvuKBBx5gzpw5nHjiiUSjUQDy8vJ2ablut9vJzMwkr7KF725MnToVj8dT9WinFr8i0tj89BMcdRRcdZU5AtWzJ8yeDTNnQps2ia5ORESkwYt7at95553H9ddfT1paGgMHDgRgzpw53HDDDZx77rm1WtzO9+vVqxe9e/emc+fOzJ49m6FDh+71fSdPnszEiROrjr1er8KUiDQu5eWwYAGkp8Ndd8G115rNJURERKRWxB2k7r77btatW8fQoUOx281vj8ViXHzxxdx33321XuDODjjgAFq0aMHq1asZOnQoOTk55OfnV7smEolQXFy8x3VVYK67cqkzlYg0JtEoLFoEffqYx8cea66JOuUU+IP/fygiIiJ7J+4g5XQ6eeONN7j77rv5+eefSUpKolevXnTo0KEu6qtm06ZNFBUV0apVKwD69+9PSUkJCxcupM/2Dw9ff/01sViMfv361Xk9IiL1wv/+Z26qu2KF2ca88v8fq4OpiIhInYk7SFU66KCDOOigg/bpxcvLy1m9enXV8dq1a1m0aBGZmZlkZmYyZcoURo8eTU5ODmvWrOHmm2+mS5cuDB8+HIDu3bszYsQIrrjiCqZPn044HGb8+PGce+656tgnIo3f1q1w883wyivmcUZG9SAlIiIidcZiGJU7M+7ZxIkTufvuu0lJSam2tmh3Hn300Rq/+OzZsxkyZMgu58eMGcM//vEPTjvtNH766SdKSkpo3bo1J5xwAnfffTctW7asura4uJjx48fz0UcfYbVaGT16NE8++SSpqak1rsPr9eLxeCgtLSU9Pb3G3ycikhCRCDz9NNx+O1R2Hb3sMnNz3aysxNYmIiLSwNU0G9RoROqnn34iHA5Xfb0nFoslriIHDx7MH+W4zz///E/vkZmZycyZM+N6XRGRBisahf79zUYSAH37mqHqyCMTW5eIiEgTU6MgNWvWrN1+LSIi+5nNBiNGwG+/mSNQl11mnhMREZH9SjsyiojUZ6EQPPww7LzJ+OTJsHKl2UxCIUpERCQhajQidcYZZ9T4hu++++5eFyMiIjv56iuzG9/y5WZb8++/N4NTcrL5EBERkYSp0YiUx+OpeqSnp/PVV1+xoHJ+PrBw4UK++uorPB5PnRUqItJkbNwIZ58Nw4aZISorC8aNgzjXoYqIiEjdqdGI1Isvvlj19S233MLZZ5/N9OnTsW2fUhKNRrn22mvV8U5EZF8Eg/DII3DvvVBRAVarGaDuustsbS4iIiL1Ro3an+8sKyuL//73v3Tt2rXa+RUrVnD00UdTVFRUqwXuD2p/LiL1wsyZcMEF5tfHHgvTpkHv3omtSUREpImpaTaIu9lEJBJh+fLlu5xfvnw5sVgs3tuJiDRt27eWAODcc+G00+C112DOHIUoERGReqxGU/t2dskll3DZZZexZs0ajty+b8n333/P/fffzyWXXFLrBYqINEp+Pzz0kBmafvwRUlPNqXzvvZfoykRERKQG4g5SDz/8MDk5OTzyyCNs2bIFgFatWjFp0iRuvPHGWi9QRKTR+egjuOEGWLvWPJ4xA666KrE1iYiISFziXiO1M6/XC9Dg1xVpjZSI7BerV8Nf/gKffGIet2kDjz4KZ52ljnwiIiL1RJ2tkQJzndR//vMf/vWvf2HZ/h//3NxcysvL965aEZHGLBaDv/8dDj7YDFEOB9xyi9na/OyzFaJEREQaoLin9q1fv54RI0awYcMGgsEgxx9/PGlpaTzwwAMEg0GmT59eF3WKiDRcViusWAGhEJxwAjz5JPyu86mIiIg0LHGPSN1www307duXbdu2kZSUVHX+9NNP56uvvqrV4kREGqwVK2D7OlLA3B/qnXfgs88UokRERBqBuEekvv32W+bOnYvT6ax2vmPHjmzevLnWChMRaZDKy+Gee8y1T2eeae4NBdCunfkQERGRRiHuIBWLxYhGo7uc37RpE2lpabVSlIhIg2MY8OabcOONUPlHJa/XnM73uz88iYiISMMX99S+E044gccff7zq2GKxUF5ezh133MHIkSNrszYRkYZh6VIYOtTcUHfzZujUCT78ED7+WCFKRESkkYq7/fnGjRsZMWIEhmGwatUq+vbty6pVq2jRogXffPMN2dnZdVVrnVH7cxHZax9/DKefDpEIuN0weTJMmgQ7rSEVERGRhqOm2WCv9pGKRCK88cYb/Pzzz5SXl3P44YdzwQUXVGs+0ZAoSInIXisrg27d4Mgj4bHHoGPHRFckIiIi+6BOglQ4HKZbt258/PHHdO/evVYKrQ8UpESkxn7+GV54AR5/3GxrDpCfDw1wNF5ERER2VScb8jocDgKBwD4XJyLS4JSUwPXXw+GHw1NPwauv7nhOIUpERKTJibvZxLhx43jggQeIRCJ1UY+ISP0Si8GLL8JBB5kBKhaDs86C445LdGUiIiKSQHG3P58/fz5fffUVX3zxBb169SIlJaXa8++++26tFSciklALF8L48fDdd+Zxt25mmBo2LLF1iYiISMLFHaQyMjIYPXp0XdQiIlJ/GAZcfTUsWACpqXDHHebUPrUzFxEREfaya19jo2YTIgJANGo+KsPSvHnmCNRDD0GbNomtTURERPaLWm82EYvFeOCBBxgwYABHHHEEf/3rX/H7/bVSrIhIwn3/PRx1FEyduuNc//4wc6ZClIiIiOyixkHq3nvv5W9/+xupqam0adOGJ554gnHjxtVlbSIida+gAC6/3AxRCxbAP/4B+iORiIiI/IkaB6lXXnmFZ555hs8//5z333+fjz76iBkzZhCLxeqyPhGRuhGNwtNPm934XnjBPDdmDCxaBA10c3ERERHZf2rcbGLDhg2MHDmy6njYsGFYLBZyc3Np27ZtnRQnIlInFi+Giy82QxPAoYfCtGkwYEAiqxIREZEGpMYjUpFIBLfbXe2cw+EgHA7XelEiInUqNRWWLYOMDHNUasEChSgRERGJS41HpAzDYOzYsbhcrqpzgUCAq6++utpeUtpHSkTqnUgEvvoKhg83jzt1grfeMtdFZWUltjYRERFpkGocpMaMGbPLuQsvvLBWixERqXVz5pib6i5ZAnPnmp34AE4+ObF1iYiISINW4yD14osv1mUdIiK1KzcXbroJ/vUv87h5c9i6NbE1iYiISKNR4zVSIiINQihkbqDbtasZoiwWuOYaWLkSTjst0dWJiIhII1HjESkRkQZh+HCYPdv8+qijzGYShx+e0JJERESk8dGIlIg0LmPHmg0kXnwR/vc/hSgRERGpExqREpGGKxiERx4xN9U980zz3EUXmVP4PJ6EliYiIiKNm4KUiDRMn30G118Pq1ZB69YwYoS5P5TVqhAlIiIidU5T+0SkYVm3zhxxOvFEM0Tl5MCDD8JO+9mJiIiI1DUFKRFpGPx+uOsu6N4dPvgA7Ha48UZYsQIuuMDsziciIiKyn2hqn4g0DAsWwB13mF8PGQLTpkGPHomtSURERJosBSkRqb/Ky811TwDHHgsTJkC/fnD22RqBEhERkYTS1D4RqX8qKuDvf4cOHWDjxh3nH30UzjlHIUpEREQSTkFKROoPw4B33zXXQd1zDxQXwyuvJLoqERERkV1oap+I1A8rVsB118GXX5rH7dvDY4/B6acnti4RERGR3dCIlIgk3h13QK9eZohyOuG222DZMjjjDE3jExERkXopoUHqm2++4eSTT6Z169ZYLBbef//9as8bhsHtt99Oq1atSEpKYtiwYaxataraNcXFxVxwwQWkp6eTkZHBZZddRnl5+X58FyKyz8Jh8zFyJCxdCnffDcnJia5KREREZI8SGqR8Ph+HHHIITz/99G6ff/DBB3nyySeZPn0633//PSkpKQwfPpxAIFB1zQUXXMDSpUv58ssv+fjjj/nmm2+48sor99dbEJG9sXQpLFmy4/jWW+GTT8xHly6Jq0tERESkhiyGYRiJLgLAYrHw3nvvcdpppwHmaFTr1q258cYbuemmmwAoLS2lZcuWvPTSS5x77rksW7aMHj16MH/+fPr27QvAZ599xsiRI9m0aROtW7eu0Wt7vV48Hg+lpaWkp6fXyfsTEcDrhTvvhCefhD59YN48sGqGsYiIiNQfNc0G9fYTzNq1a8nLy2PYsGFV5zweD/369WPevHkAzJs3j4yMjKoQBTBs2DCsVivff//9Hu8dDAbxer3VHiJShwwDXnsNunY1G0hEo9C6NZSVJboyERERkb1Sb4NUXl4eAC1btqx2vmXLllXP5eXlkZ2dXe15u91OZmZm1TW7M3XqVDweT9WjXbt2tVy9iFT5+WcYOBAuugjy8sype59+Cu+9Bx5PoqsTERER2Sv1NkjVpcmTJ1NaWlr12Ljzhp8iUnvmzoXDD4f//heSkuDee821USNGJLoyERERkX1Sb/eRysnJAWDr1q20atWq6vzWrVs59NBDq67Jz8+v9n2RSITi4uKq798dl8uFy+Wq/aJFpLp+/cy1UB06wCOPmHtDiYiIiDQC9XZEqlOnTuTk5PDVV19VnfN6vXz//ff0798fgP79+1NSUsLChQurrvn666+JxWL069dvv9cs0uQtXAhnngkVFeaxzQZffw1vvaUQJSIiIo1KQkekysvLWb16ddXx2rVrWbRoEZmZmbRv356//OUv3HPPPRx44IF06tSJv//977Ru3bqqs1/37t0ZMWIEV1xxBdOnTyccDjN+/HjOPffcGnfsE5FaUFRktjB/9lmzscTBB8OUKeZzqamJrU1ERESkDiQ0SC1YsIAhQ4ZUHU+cOBGAMWPG8NJLL3HzzTfj8/m48sorKSkp4ZhjjuGzzz7D7XZXfc+MGTMYP348Q4cOxWq1Mnr0aJ588sn9/l5EmqRoFF54ASZPhuJi89z558NVVyW2LhEREZE6Vm/2kUok7SMlshe+/x7Gj4cFC8zjnj1h2jQYNCixdYmIiIjsgwa/j5SI1HMPPWSGqPR0ePxx+PFHhSgRERFpMupt1z4RqWeiUfD5zOAE8OijkJEB99wDf9Als74wDIOSijDBSAyX3UpGsgOLxZLoskRERKSBUpASkT/3v/+Z0/h69oRXXzXPtW8Pzz+f2LpqKN8bYMlmL5tLKghFYzhtVtpkJNOzTTrZ6e4/v4GIiIjI7yhIicie5eXBLbfAK6+Yx+vXQ34+ZGcntq445HsDzF5RQKk/RHaaG7fDRiAcZU1BGYXlQQZ3zVKYEhERkbhpjZSI7CocNtc9de26I0RddhmsWNGgQpRhGCzZ7KXUH6Jj8xRSXHZsVgspLjsdm6dQ6g+xZLMX9dwRERGReGlESkSqW7HC3FR3yRLzuG9fePppOPLIxNa1F0oqwmwuqSA7zb3LeiiLxUJ2mpvNJRWUVIRpluJMUJUiIiLSEGlESkSqa9UKCguheXNzg93vvmuQIQogGIkRisZwO2y7fd7tsBGKxghGYvu5MhEREWnoFKREmrpQyGwgUTm9LT0d3n8fVq6EK64A2+5DSEPgsltx2qwEwtHdPh8IR3HarLjs+n+FIiIiEh99ehBpyv7zHzjkELj4YnjttR3n+/WDzMzE1VVLMpIdtMlIJr8ssMs6KMMwyC8L0CYjmYxkR4IqFBERkYZKQUqkKdqwwVwHdfzxsHw5ZGWBy5XoqmqdxWKhZ5t0PElO1hX58AUjRGMGvmCEdUU+PMlOerZJ135SIiIiEjcFKZGmJBiE++6D7t3hnXfAaoXrrzen8Z19dqKrqxPZ6W4Gd82ic1Ya3kCYTSUVeANhOmelMfggtT4XERGRvaOufSJNyfnnw7vvml8fc4zZja9378TWtB9kp7sZkuaipCJMMBLDZbeSkezQSJSIiIjsNQUpkabkL3+BefPgwQfhggugCQUJi8WiFuciIiJSaxSkRBqrQMAMTElJMGmSee7YY+G338Ct6WwiIiIi+0JBSqQx+ugjc/SpMjRdcAG0bm0+pxAlIiIiss/UbEKkMVm9Gk46CU45xQxRbdrASy+Zm+yKiIiISK1RkBJpDCoq4Lbb4OCD4ZNPwOGAW24xW5ufc06TWgslIiIisj9oap/sN4ZhqGtaXdmyBR5+GEIhOOEEePJJ6No10VWJiIiINFoKUrJf5HsDLNnsZXNJBaFoDKfNSpuMZHq2Sdc+PnsrPx+ys82vO3eGhx4yp/KdfrpGoERERETqmKb2SZ3L9waYvaKANQVlpLsdtM1IJt3tYE1BGbNXFJDvDSS6xIalvBz++ldo3x6++27H+euugzPOUIgSERER2Q8UpJoAwzDY5guRVxpgmy+EYRj77Z6GYbBks5dSf4iOzVNIcdmxWS2kuOx0bJ5CqT/Eks3eWqmp0TMMeOMN6NYNHngAgkF4551EV7VbdfE7JyIiIlKfaGpfI1cXU+riuWdJRZjNJRVkp7l3ux4qxWln2ZZSOrVIpmOLFK2Z2pOlS80Rp1mzzOMDDoAnnjA79NUzmsYpIiIiTYGCVCNWOaWu1B8iO82N22EjEI6ypqCMwvIgg7tmxf3BNt57BiMxQtEYboet2n1KKkKsL66gqCxEgS8AFgvdc9L1YXt37rkH7rwTolFzD6i//c3cYLce7gdVF79zIiIiIvWRpvY1UnUxpW5v7umyW3HarATC0apzJRXmdXneAHYbtEh10SxZa6b2qE0bM0SddhosWwZ//3u1EFVfptFpGqeIiIg0JRqRaqT+aEqdxWIhO83N5pIKSirCNEtx1tk9M5IdtMlIZk1BGR2dKQCsL67AF46QneIkvzxIK08yWWkuslJdrCvysWSzlyFpriY3za+yPXz0p0W4SopJHTXc/BmMGQNdusCxx+7yPfVpGl1d/M6JiIiI1FcakWqk9jSlrpLbYSMUjRGMxOr0nhaLhZ5t0vEkOVlX5KOgLEhRWYgku5X88iApLgcdmidhwbLLh+2mJN8b4NsfVrN17JVkDuqP9dJL+GbhOnN0zmrdY4iqT90Q6+J3TkRERKS+UpBqpHY3pW5ngXAUp82Ky17zX4G9vWd2upvBXbPonJXGtgpzTVQ4ZtDKk0yv7SGrUlP8sJ1fUsG6h5/miBFH0e3tl7HGYpQcegQbcgv2GIjq4zS6uvidExEREamvNLWvkfr9lLqdp1oZhkF+WYDOWWlkJDv2yz2z090MSXPRqUUyWCw0S3aQlebCgnkPAwNfMEqpP0QkauCwwjZfiGAkhstuJSPZ0aCm+lVO0/uz+o0FC3BdfjVH/rwQgPIDDmTFrfex7ahjyTGMPU51rI/T6Orid05ERESkvlKQaqQqp9QVlgdZV+Sr1kEtvyyAJ9lJzzbpcYWTfb2nxWKhY4sUuueks6agjKxUF1ig1B9ifZGfwvIA+WVB0t0OXp63HrfDhsthbXDts/9o3VJWmqsqYCWtW0N6v354YjEiySn8du2NbLzgcgynGXz+KBDVZBpdoS+4X0f26uJ3TkRERKS+UpBqxCqn1FV+qC/0BXHarHTOStvrULKv9/z9h22nDX7cUMq2ihAOmw2P207MMFia6yU7zcVh7TNw2W0Npn32H7X/XltYTkayg8LyEBWhKMlONyOHjsIXNci79W4iOa12ud+eAtHO0+hSXLv+n3GiptHVxe+ciIiISH2kINXIVU6pq8k0s/11z8oP25/8kstbCzeTXxbAYbOS5LCS6nKQleqiW8tU8suDbNoWoFfbdDo6U+p9R7/fr1uqrDHFZadZ1MGKD7+m/5tP8fYN91GW1RLDsPDomL8TiFk4OsnDrjFqz4GoPk+jq4vfOREREZH6RkGqCbBYLLW+TmZf71lYHuSnDSUEIlE6ZiaTmuQgGI6ytshHRTBKZqqTjCQnRb4gvmCUVJe93rfP3tO6JXtxAZ3vuoMzv3wXgJPe+gf/ufl+wpEYxbEohcUB5q8r4aTebqyWHYHpjwJRfZ9GVxe/cyIiIiL1iYKU7HexWIzPl+ZRXBGiZZoTT5I5wmTBQrrLgT8c4bdCH33bNyMSixGOmtPa9nbdT00bP+yrXdYtRaO0feNlDnjyAZxlpQB8038k34+dgNViweWw0cqTTHkwSr43wLItZXRsnlLjQKRpdCIiIiKJoyAlf6guQsj6ogrWFPhonZHEVm/A7NJnt2C1WLBZrSQ7HZRUhCjyBXHYbThs5ijN3qz72Z8b1u68bqn10h/pdu/fSFu+BIDf2h7IzIsmsapzb7pkppFU+U0WC22bJROKlNMs2YE3EI4rEGkanYiIiEhiKEjJHu0uhLT2JNEuM5n0JEfVh3Ygrg/yZcEIwUiUthluKkJRtvmCZNicOGwWkp02vP4w4UiMoooQB7fKIMVl26t1P3/U+KGgPMBh7ZpVex/7Gj52XrfUe9bnpC1fQjjdw8LLb+ShjgOxOhxkux24fxcEDcDttHFkx+a09LjjDkSaRiciIiKy/ylIyW7tLoRs9Qb44tc8QpEYHVukkJniJNVlxzDAF4r86YhP1ehWKIYVC75QhOw0F/5glBJ/iBSng1SXjaLyAGXBKFYstMlwURGMf93PnzV++GFdCYs3ldKxRTIuu23fR6nCYSyFhfRs04yC8gCfnXYZA/1Bfrt0PN6UdIwfcwlHDbJSnbs0hijyhWie4iI73aVAJCIiItJAKEjJLnYXQkoqQvxW4CNmGMQMg3DEwDAM/rMsHwsGR3ZqTtuM5KoRn9+3Kt95dCsQNkekftnk5bB2Hjq0SCa/LEhheZDSiiDeQIQUlx0Dg582ltA+M5luOZ5dgs4fTTvcU+OHUn+IJZvLCIYjGDErzZJd2K2WfWuvPmcOjB8Pqanw6Vc4rFY2R+xMG3kVbAnTPLWcrjlpFJQF8IUi2G3mFMBQNEapP0Q0GuPw9s0UokREREQaEAWpRmxv1zf9PoQYhsH64gp84QjZaW5CkRjbKkLmprIOCxaLlYLyIK08blJcdjo4k1m+pYxvVxUypGsW4WiMOSsLq0a3stPchDoafL40j+/WbqNnm3SaJdnZ6vUTCBu0z0zmxJ45pLgd5JUGSXLYObh1WrWAk+8NsHhzKavzy7fvyWSjS3Yqvdp4yE43p8eFIjEiMWP7HlVWUpxW1hf58YXCtPEkUVQRImYYpLgce9dePTcXbroJ/vUvAGKZzVnw1QKKstrQt0MzogaUB8KU+MOkOG1kp7nI8wYorQgBFsDAarVwaPtmHHNgC61rEhEREWlAFKQaqX1psvD77nO+YJRiXwiP2wxiDrsVX3mQilCEFqkuDKDYF8IXjBI1Yqwv8pNb6mdZnpeCMj/+cAyw0LP1jml5XXPScdttzF6Zz7pCH+FoDH8oRuesFA5r34yOWalYsJCV6mJdkY+luWVkp5vBLt8b4KOfc1mxtZyYYWCxGBiGhTUFPtYV+jj5kNZ4/WHWFvoIRMqwWcFutZLstFFUHqR5iotQzMBus+KwmuuVLBZLzdurh0LwxBNw111QXg4WC8ZVV/Hfi//C5rC92lRCT5KD1hkG64p8dGyRQq82HtYU7hT+stLo1dajDnsiIiIiDYyCVCNjGAartpYzZ2UBFaEIHTKTSXLa9zjlbnd27j6X4rITjsWIRGM43WaTh3Aktn2kKobDbgUDygJhiiuCbNg+4uNxO7AYYBiwPK+MzGQn3swkPEk7AkqHFimckdKGhRu2scUbwGGxkp5kJ7c0QDBi0KG5ef3OAScj2cH/Vhfy44YSkhwWmiW7cNithCMxtlUE+f63IsqDEdJcNkJRs3V6VqqbSNRgS2mArd4AaS4HFZEoOeluUly2qnpq1F5940Y44QRYvtw8PuooePppSrr2ZP0vuWSnOXZZA+ULmt0G88sCDDywLX06ZqrDnoiIiEgDpyDViFROd5u1PJ88b4DsNDeRmEGHzGQykp01nr62c/e5js4UHFYr9u1retwWKyX+EC1SnfiCEcKRGAZgs1nZUhrEFwrTMs1NIBLD6bCS5LCT7rJTFgyzdLOXXm09pLrtWDCnDJb4I6wr8hGJQo/WybgddsKRGFtKK/D6w/Rqm06qy1EVcIp9QeauKSQcjZHjScLlsAIWooZBIGywxevntwIf7Zsn0bF5KhCmcPtoWlaakw3FPpZvLaNbThodMpOr/Qz+rL26YRiUeFqQ6krC1iILy4MPYBkzBqxWgqWB6ntIASUVIdYXV1DsCxEKxygPhWmR6ubYA1uQ49m/I1D7ay8tERERkaZCQaqRqOyyl1fqJxCJVYWEPG+AMn+Enm3S8SQ7SHHZWZbnpVOLZDq2SNnth2mLxULPNukUlgdZV+QjK9VFsyQnG0sqcFgh1e2ka8s0NhT7yS3xYbFYyEh24guGyUhyYgClgTA5lY0myoMEghFySwJ4A2FaZyTTLNlOkS/ETxu2ke8NkuKysWlbgLbNkkh22mlpd7O1LMD6Ij8HZFlw2qx4/WHmrS7kl02lJDttlAfCZKa4SHXbKPCGCEQipLntlPgCpDgdlAXD5ia/bjv+cJRwNEa6y0FFOEqnFilkJO8YHdtje/VgEP7xD/LPG8OSYnPtmPuWh4g2zyK7XQ49y0Nkp7t3GcUrqQixZLMXXziCZ3vLc8MCm7dVMHtFwd41tdhLtbWXlsKYiIiIyA4KUg2cYRhs84X4dlUheaV+Wqa72Vzqx+WwYbVYcNmt5JcHWbbFS7LTTqHP7I6HYdC91a6d8Cplp7sZ3DWr6gO43Q5WwGq10TkrmTS3g6y0CGsKDCzE6NDcTok/RNQwyC8PkuKw0yzJwdpCH5GYQcxiwW0Hp93Kb4VlbCsPk+qyURaImGuKrJBX6icSidExK4Vkp52MJCeF5QHsVmiXmcycFfnMX19MeSBMNGYQCEXZVhECi4VUl53sVBdef5gYBskuG1mpLraWBUhy2OjUIoVAJEb7zGSW5Xop8gVpluys2ltqt+3VP/0Urr8eVq+mYOl61lw03mwF3+vgXaZKZqW5qkbxOjiSdzTnSHVhAbaWBWiW7KRlupu8Uj+LN5dyXE2bWuyDP9pLK54uhftzY2MRERGRhkBBqgGr/HC7Kt/Loo0lpLgclG2fbheOxHA5bFgsFpw2C0tyvbRMd5KR5CQrxU2zZOeffpjOTnczJM1VNQrh9YfZuK2C3BI/m0oqcNqsDOuejWFAQXmQcn8EYpCTkUT7Zkls2OanIhLlwKxUfiv0URaIYLVALGpQEggRjNpw2a20yUzCgoVI1KCwPIjdbqVLVooZysqCdGqRQokvxA/risEwSHbawQJOh42KcIRt5SFsgJHqxBc2mzg4bBawWHDYbCzeXML6YgdsDy0uh5XsVDfeQJhCXxCnzUrnrLQdoWDtWpgwAT74AIBgi2zy2nSiQ/NkKkIxvIEwDpuVDs2TWV9UYU6V7JZVNYq3LK+MvBI/aUkOQpEYed4AFaEo0ZiBNxAmGoP8siDtmiVzUE5anf1+/NFeWvF0KaytMCYiIiLSmNTrIHXnnXcyZcqUaue6du3K8u0L/QOBADfeeCOvv/46wWCQ4cOH88wzz9CyZctElLtf7fzh1m23kep0kO42p5SV+iOEogYdM5PBAtsqwgTCEZolpVIRiZGT7iYrzUUWrj/9MG2xWKo62OV43BzYMrXa9C5Pkp1tvhBr8ssJh2OUhcL0bJWGP2xUdfpz280mEp4kB/5wjLyyIC6buQ6pWbITu9WC22GnQ/MUHDYLxRUhckttOKwWctLddMhMZuYPGyj2hUhz2bBawRuIgAEOu7VqylmSM4jFYtCxeQrBSIzyQJh1ReXkbvOTkeLAZrUSCEVpluLAH44wuGtL0pMcVe+jtLiMssn3kvr4w1gCAbDbCVw7nvdPvpRwSioFm8so8gWJxGLYrVaap7jISnNUNcKoHMX7dmUhy7d4wQrRKFSEorgdVlKcdixWC9FojC2lfuasLCAj2VFnIWRPe2lV/rvuqUvhzlP4nDYLizeX7nMYExEREWls6nWQAjj44IP5z3/+U3Vst+8oecKECXzyySe89dZbeDwexo8fzxlnnMH//ve/RJS63/x+pMEXjOJ0WLFYLGajh3CMilCUrd4AbqeNbT4zbG0LhMlMclVrslDjlt/b7Rys8r0B3v8plx83bKPIFyQQjuEPRcktCdC1ZSqhcAy33Uqe10+qy8GB2SkU+4JsKK7AZrFQEYrgDYRZnlfGgdmppLgcHJCVSpLXz8Gt0qkIRWnXLIlft3jZUhogx+MmxWHHZrMRjPgoD0Zwx8wpjL5whEgUDmyZRrecNNYXV7B0i5dN23zm5sHmTw6HzUYwbPD92mJapLo5/fA2FJQFmb2ikDa3TqTrezMAKO43AO8Dj+HtfCDrftmCr7ycQDhKRpITh91R1RCjpMJO81RnVae/7HQ3Q7plUegL4rJb2bwtQCgSxTBgQ3EFUcMgFjNwbO/iV5ch5Pdt7H9vd10Kfz+FLxKNsbHYT9ec1LjCmIiIiEhjV++DlN1uJycnZ5fzpaWlvPDCC8ycOZPjjjsOgBdffJHu3bvz3XffcdRRR+3vUvebkoowm7dVmE0N/GHsVguZKU7yvAFcqS5y0t0U+UJkJLsoKA9QWB6kdXoS7Zsl07F59SYLNWr5vRv53gAfLspl0cZt2GxWWnmSsACbtvnZUuKntCJEOBrFWmLFYoEkpwNfIMzm0gAlFSFapDjJSHaRlepifbGPxZu9dM1JxW23Ybfa8IeitPK4AQu+oNnAwWG1YrFaSHM76Nwilc2lfgwDUrFRFjTolpNKn46ZZCQ7qQiFyd3mp9gXxm6xYNv+fVlpLiwWKPKF+N+aAg5ulcbP20Np5LLxtP1xHnPH3MBHBx5DZGOMNuWbWbSxFJvFQvdWabi2hxKXw0ZLu5uN2yqIGQZO246Q0SzFyYHZaSzZXEJheQBvIEI0ZpDismO1wLaKEBbDoCwQZnleKYe1z6iTEPL7Bhi/9/suhbubwpdX6ifPG8BqsZDstFdrXw97//sjIiIi0tDV+yC1atUqWrdujdvtpn///kydOpX27duzcOFCwuEww4YNq7q2W7dutG/fnnnz5v1hkAoGgwSDwapjr9dbp++htm0uMUdbLBaIGQZ2qxW33YYFs9FDmsuOzQZtmrmx28wPzIe2y6Bz1q6jCn/W8nt3DMNg8aZSVuWXkeS0VZs61trjpjwYYkup2aHPF4zisFlIdZtT6Fx2Cy6blVxvgK4t02jlcZPutrOyoIxNxX4cdis56W4ObuOhXbNkvl9bRMcWyazKL6csEMJld4PFgttpp12zZMqDEQCaR2Jke9w4bFaKfUGWbPbiDYRJcdppmebCZrUSiUXxBaNkpTmxVlRw1IvPwYwwpTfeTcfmKXhTnDz65Pss3VJOxTYfgDmaFTMoi0RYV+ijVUYyLrsVm9WCy26uw8IwW52HokZVN7uebdJZnV/Gb4U+nHYrLZKdRAwoC0VIdTpol5lEeTDChuIKAuFoLf1mVPf7Nva/399q5y6Fe1pPVbmPV6k/xPoiP73aOrBQ85bxUrvUOVFERKT+qNdBql+/frz00kt07dqVLVu2MGXKFI499liWLFlCXl4eTqeTjIyMat/TsmVL8vLy/vC+U6dO3WXtVUOR7w0wf+02tlWEaJnmItXtJByJUeIPVbX69vrDlAcjBCMx+nbM5MDsNIp8wV3uFTNirC/y0aZZMoZhYBhGjT6UlVSEWV1QRixm4El2Vn2PLxhhfXEFpf4oxb4Q4WgMh92CzWIhFIlSFgjjsltpnuLEbrHiDUQIhKMkOe0c0DyFzSUBerfxMKJnKw5smcpWb5BQNEYbTxKds1L4eVMp2yqCpLqc2G07XjMrzc2xB7egXbNkckv8LN3ipagihMtuJTPFScr2jYQxrPiCYbrPncPYd6bRotj8Pck9/xKKU7rxy6ZS1hRUYBgGbTOSqAjH2OINYMQMkpw21hVVkOcNkJnsIBw1MCwWmiWZIeTTJXm4nbZq3ewOb9+ML5dtxWoYlIUi2KwWmiU7yUp1keKyEzMMiivCdRakft/GfudGEb/vUrjNF9rteqoUl43mqU58oQiF5QF8wRRSt49u7bFlvNQJdU4UERGpX+p1kDrxxBOrvu7duzf9+vWjQ4cOvPnmmyQlJe31fSdPnszEiROrjr1eL+3atdunWveHylGDcDTKQdlp5JUFSGPHNLPKVt9pSQ7aZSQzpFsWzVKc29cAFbC2sJxUlwOr1UKpP8jq/Aoi0RgxDD5ZHKnxh7JgxFyDBeb+TpW15ZcFCYajBIIRIjEDp91KZqqTWMyg1B/BMGKEogahmMGh7TyU+MNsqwgTNUIYGHiS7Qzpll3Vya5yalowEqN7q3TKgmFySwKUh8JYLOZokd1q5dB2GYzs1YqsNBfrCn14A2GyUpwUlYcxttdmsVhonb+BS994jEOXzwcgv1lLvrn2VlJ7HMz6LeWUVISwWsxRGIvVSpLTQorTRok/TOn2Tn2+UJRoDMIxg0jUXA+Wmezg4NZptM1IrtbNrnurNA5okYIvFKFFigu7zYrbbt0+imXgC0VIddn3uIYpnt+LPY1S/L6NfWF5kEjMoEWqi15tPGSluar+TXe3nspisdAhM5nSijC5pX5K/SGS/qhlfA1qkvipc6KIiEj9U6+D1O9lZGRw0EEHsXr1ao4//nhCoRAlJSXVRqW2bt262zVVO3O5XLhcrjqutvZVdmFrmZ5EZkqMskCE/PIgHrcDp81KksPG6oJyjjqgOcce1ILMVPM9Zqe76dkmnc+X5vHLZi9ef5jSihDpSQ6OOqA5XbLT4vpQ5rJbSXbaAMP88G21EdjeJQ8LlIWiOG0WLBYLSXY7DpsFh81KkQ/SXHYwINllJxozcDms+MNRQmEDiwXWFvpome4mO91dfWpa8xSO7Nic9cUVbCkxNx32ByMc3NbD2X3bVtWb5LTjdtpome6mZZqTPG+ISFkZY/7zKifPehNHNELQ7uCtIefx3VmXc3CXHEIVYYp8QVJcdooqQlWjXZX1OUMWinwROjZ3k+eNkeS00irJSak/xBav2cVv87YALVLdeJKcdHSmsLbIx5LNpTRLcRKOmqEpI8lJDAiHo5T4QzjtNto1S96nIFWTUYrKNvartpazeFMphb4A2yqCfL+2iE3b/PRsk/6H66kykp10yU4lZhgEwrGq1vfVWsbHWZPUXG21sRcREZHa1aCCVHl5OWvWrOGiiy6iT58+OBwOvvrqK0aPHg3AihUr2LBhA/37909wpXVj51GDZKe5yez6Yt/2kRQLdqv5l/++HTPJSnOxzReq2v9p8aZSUlx2+h+Qyaqt5eR5baS57BSUhchON0dbPEmOGm0Wm5FsNntYtsVbtQlwJGYQNcz1RKFIFLfDhsNmNprAYiHZYaPcbsMwDHNKW3mIbf4wVquVrFQn2/whmqe4ydv+l/fKMHdw6zTWF/n4ZXMJrdKT6JGTRqt0N1u8fjJTXIzsmUNLz47RycpAYLda6NYqHW+gGMojDP7+UxzRCP/tehQPnXgVZW06cmhaKv5wjML8csLRKKkuBzaLuZ+V3WZOG0xx2cxwFoqSVxomakCLVBfRGHj9UTKSHKQnOSnxh6vWEHkDYQrLQvy8sQSn3cI2X5hUl51oLITDbv475aQnYbVa6JaTvtfT4uIZpSgoC7JoYwml/hA56Um7XDvooBZ/uJ4qGIkypGs2h7XPqLYW7Pe/I3uqaXVBGeuLfBzRKZM2GUkaoYrD3raxFxERkbpVr4PUTTfdxMknn0yHDh3Izc3ljjvuwGazcd555+HxeLjsssuYOHEimZmZpKenc91119G/f/9G27GvMiRs9QYoKA+a65Ai5vqaFLfZVCF5+zqdWcsLzBGBSIy1hT7C0Rh9O2TisFsJRWNkpbqwWS1sKTWne6Un2YkaRo02iy0oC1IWjBAIx9hQXMGG4gqaJTsJhKJUbF/vk+y0keKy4Q2EcdmsGFhIclgxDKgIR8gr9ZPictAi1UFpIEya20nXnFTS3Y6qv7AfbBgszS3DH46S7w2yIq8ct8NKh8xkDm3XjF5tPbuMcFSOYq37bhE0a4XVaqHI5mbqydcTNKx807UfLZKdHN4+g95tMygsD1JQFsQbiODKtJqjUmUBs4GHw05GspNSf4SMJAfFvhDJTjvBcIxwzMDttJKd6iJiGKQ67RSWB1iz1cqaQh/+cASHzUL3HA9rC33klwVw2m10yUolxWWnPBghYw/T4moinlEKYJdrzTVx4ElysKXUz5LN3hqvp2J7M/ma1hSJxSjzR1iVX8aKrV56tEqnbbOUJjtCFe+0x71pYy8iIiJ1r14HqU2bNnHeeedRVFREVlYWxxxzDN999x1ZWVkAPPbYY1itVkaPHl1tQ97GKiPZQYrTztfLt5LktOFJcuJxOwhFY5T6QxR4A/TtkMlPG7fh9YfJTjNHigKRMsLRGEtzvWSk2Nm4rQILFvNDsjdIDIPebTy0y0wmFImxvrhij5vF7jziMKBLc1oVuFm5tYy8Uj/+cJT0JCftM5PxBiJ4/RG2+cOEIzGsVgspTis2qxWX3UZFOFoVUlp5kujYIrmqtXZ2mpvleV7WF/mIxGKkuuy0SHXhD0fZVhEmEvXRqUXKbn9GlrIyDnlsCoNe+D8ev+hWYocNJRKJ8UHHIwhHwRWKEEt24LBZSXHZaOXxgGGwrriC/LIQTpuFSMxsxJHjcZDssOLf3iiiRaqTLtnpuJ02wtEYG4t8YLFgs0DMMLspri3wURGOmoHWbnaz69OhGeuLKliZX8aaAh8Ht0qnyx6mxdWEYRisK/SxLM9Ls2QH/O4z+O9HKYBqIxolFSHWF1dQ7AuZa+RiBgVlQdplJldfT+ULVk3hy/G4WJpb9ofT9XY3clLqD7F4kxdfKEx2motIFOxWa5Nd27M30x7jbWMvIiIi+0e9DlKvv/76Hz7vdrt5+umnefrpp/dTRYlndts2RxQsABbzc7Q5ZQ42l/jJSnfRqXkKWCB3m59gOEqLVCeFZUHWFJRRHoyQ5LBR7AtRHgwTi8HS3FL84Rgt011kp7mpCEV2WXexuxGHHE8SvdtmUFoRZlV+GTHDoCIU5dtVhQTCEZKcdmx2C4GIQZEvjM1qoY3HTSRmwReKkBI2g4Cx0yCHy2FlQ3EFWWlO2jdLYWmuF184Qnaam7aeJDaX+vlu7TbCMYMhXbPND6CGAa+9hjFpEs23bgWg12+/8GGPwQSiMexWC267tWr908qtXqIxg34HZNI5Ow2Xw0p5IMqmEr/ZpS4QYU2Rj1jU/Cu/J8kMsaluG26H3RxVcDvYWFJBVqqLzSUVlPojuB3mVMWyYJRIzOC3Ah+92nro3dZDK4+bbRUhjjmwBR1bpOzVSFS+N8DiTaX8tLGYXzaV0iLVRZuM5KogamDgC0YJhKOUbO8IaLFYqkY0SipCLNls/jw9bgdOt4NAJMqG7eH51ENbM6RbVrURk1AkypyVhX86hXDnkRPDMCgPRliaW0ZxRYB2GckYWCjyBXHYrXRMa3pre/a2YUQ8bexFRERk/6nXQUqqK6kw25ofdUAzCsrMBgmRYBi71UrrjBRSXBYWby6jU1YK3oC5Zie3pILNJX6KyoP4w1HCUYPMFAcbi/1UhCLmh14MDMPC+sJyCsv9HN4+kw6ZybusuyipCLNpm48Up7kRsMNqjuqkuR2kuR00S3GybEsJK/LMQGW1WKgIRohtT0kOqwWrxYo/HCM71Y4FKPOHWV/oo8wfoWebdDKSndsDXoRerdPZsK3CDFGpOz5sZ6e58YUibC01/7o/5LflWK67Dv77XyxAYasOzLzwRhZ1OwJLoZ9Ul4OI3SDZaX5wjcQMrMDm0grWFbo5uE064ZiB02Guh3I5bHiSHJT6I5RUhHHawGa1kFtibk7bqUUybocdLJDqtOP1R/CForRMd+IPRagIRUl12mmXmYQvHGV9cQW923honurCHzHbve9tiPpwUS6r8svwhyKUBaIEQxXklwXZ6g3QvVUa2yoiFPmCVIQihKMG89duo1urNJw2c2RtffGuP0+bxUJ2mgt/ZXje3u0RzA/qs5aX1GgK4e+nnm4p8fNboY8ku5VozIInyY7dZjU3Vm5ia3v2pWFEPG3sRUREZP9RkGpAKv/i3zYjmRxPEr5g1Nyrafs0taLyEMFICb5ghJV5AXyhMBnJDlqlJ5Hn9bNte1OKFJcNw4BYzCCEgc0CgWiEUMzAH4mxaZufbjnphKKxausuNpf4+XWLFytWokYMu83cp6lDZjIZyU6C4Ri/bPKSW+InxWknHIlSHooQi4J9e40GEIxG8UcMnA4LkViMqGHgC4dZX1xButtOXmmQVJcN1/ZRM4+7+hoSh91KNGjgSXaQ/swT8Mz9EItBcjLrr53IpLbHUY6NrZu9BMJR7FYLBpgt2W0WApEodrsNYrCl1E+Ox0VhWYjsNBfDumdREYpR5Auyams56S4bYQPcNivhSIwVW8u2f5h1cUCLVLq2TOPnzaUkO6047TbKg1E8SXZaZyST4jKDQ7EvhC8YxWKhagpWvOtkDMPgv6sKWbRxG0lOG1lpbiIxKPKFiMVirMjzklvqp5XHjcftIBi2kOVxkeetwB+JkOK0s764gqLy3/08DYMSf4hWnmTaN999eK5po4PfTz112q0kOa0kO+yU+IJsLfPTu00GKS5zrU9TWtuzrw0jdmljv9O0y6a61kxERP6/vTuPk6uq8///unvtVb0vWTorCZCFsBoRAWFYxx8go6h8HeDrF1FhGNcBRgeQWVAcWZxBdJQRRx1AHJBFcWQNBEJYA4QlhJCNpPet9qq7nN8f1SnTSWdpCOl05/N8PJpHuureW6dObnfqzTnnc8RYkyA1jmy7ViK2zXqJIFA4hsHGvgIF16MpHgJNozEBA4USvq/wCBjIl2mI2+TKLqWyh2kahEyNiKHTlHDoyZZZ/k4vs5pi1XUXlY2A+xjIV9a6pByHsh/QkS6SKXgc3JrglU39dKSLeIHC0BU+YBkGEKAU5Eo+YbsyGmEaGiXPR9dM+vMuybDJ+p4sxbJPTdRicipUKQJRqGzii1LV/ZcyBZeyG6ABnQfMQwsC+NSn6Lnqn/jVOy4bXt6M65cpeAGBr/D9gACNoquwdDCMSmU+29Qpej7regsATK2LVAsxbBoasZtSE6Y/7/JmRwZL12lOhujNlsmXfTYN5MmVPSanwiyamiIaMlndmaM/VyRqV8KCbehkii5l32ew4DKzIU7Z83nszYFRrZPpz5V5cUM/hqFXP4w3JUIU3co0vmzZI130mFYXYbDkEXMsDmiKkwxXinfUxxzClkFXpkhbbYRAVaordmVKOFZl092QZdA7VOlxi9EWOth66qmhaxiajhcEKA0q//mz/Wltz54oGLGljL3szyWEEELsGyRIjSO7WiuRLblMSoVY15tjSk0EqHxYBWiKh2gfKKFQZIoeqYhDMmyTNzzqonb1w28qbJMpupW1P/EQiVBlvcurmwZJF11akiH6cmXiocpGso6p05Ut8vLGflZsHCBkaBQMjbJfWcNlGTqBUviBwgsCvKBSnCEZqqyNijkWHekCqzt9ckOb/FqGxrvV6YiV0aHaqEPUNqh9cyV1b69i3TGn88q7aUKT53HwY8/QduyRPPHiJtZ296FQ+FRGn/JDa5yUUugalH2FicFAvkTMsXBMnbhjEHMMyl7AO91pNg/kt5qSVhmx6c4UaYqHaIyHaYg7pAuVNUZuEGAZGjURm1jIYm6zxqvvBnRmiqTCNr5SBIGifbBISypMc9LZrfVG2+rOlOnNlWhJhodNC2uri7JpIE/7IBQ9n/6cx4zGaHWUECpTIdNFl0On1rChL89g0aOcrYRUgATwVmeGTf0FUhFrWLAZTaGD7aaeZksoYKDgMSkZJhE2KXkBuZJP1DH2q7U9e6pghKZpE34apBBCCDFeSJAaR3a1ViIVdZjbkmTTYJH2wRJuEFB0PcpDH14dSyPmWOTLPg0xGw1Fumjg+j6uD7alsXmwQKboURO1eacny+9f6aQxYfPYm10UvaAyipEu0ZtzaasLY+k6vZky63pzZIsuTQkHN1B4foCmVUIRmoauKwKPykiQoxM2dcpKEbUrHzDjjkVLMswhU1Ks7srSl6uM+vhKkSsGmH19nHzvf3D6M/fjWjY3H/0R1gc2hg/LIy2UOjK8uKEfy6zsh9Wfg4LnoagU49B0jSBQQ2t0FINFj0BpfGR2Hacc3MzDb3bx0oYBvCCoTkkLWyZdmSLv9hUw9MoHWNvUUUrDtgIm1YTpz5fpzVfe/8GtSZJhm/mTE6zvLdCTLdKVKdGcCDGvNcm8SQle25x5jxurKlDbFegj6pi0JMP0ZEqUPIN5k+LMaowPO3/LaMekmjDHz21k+Tu99OUUiZBFImyh6xqBH7CmO8PU2ijloZL6sH14R6M6pdTUNbqzJWYNhaHOdGm7qaczGqOs7szi+wFhyyBd8CoVJrPBfrW2RwpGCCGEEBOPBKlxZldrJSxD55l3elj57iCZsofvK4pegAoUaIrOTAmNylQxQ9NIhAw2Dbjkyz5BLiAA6qI2c5pi+AG8/O4AnYMFBksuBzTGcSyHmGOxoS/HG+0ZgkANjSwZmJqGY5noqozrKwJVGQ3SUbiBqoaauG3iDoWCzekiIcugNmbRkorQPzSqkQxZDORd3LLLXyx7gAsf/BnJQgaAN446gSwGNWGbg1sTDBTKLF/bR0+2svA+FXFRCkppH0PT8AMFqvL6UdvAMjTiIZumpMOHZtRTF7MpDk1zm9sUo+QrTF1HR8PWdbJlj/qoXQmFVMKgoWmYhk59zCFd9NDQquE25ljMaNAwdZhRH+OjBzQwuyn2vtbJNMQd6mIOPdkSU2qMyhy6Iaau4QWKVMSieasRqy22jHaELIN5rQmWru6hI10kYpsM9OUoewElT1EbsdE1eG1zhsZEpY1bh/eVm9MUyj6ZUqUaYKHs05IM85FZ9WiaNuLU05hjkgpblcIngwVyZZei5zO7MbFfre2RghFCCCHExCNBahza2VqJIKgUn/CVIu4YbOwv4vkBUcsg7Fj4QaUE+WChTLZUWWcUsXVCll6pNucYJMI2Xeky9TEbTYN0yUNHQwN0TaM2apOKmLywfoBAVxzQGKVzsMS6vhwbe/OUg4AgCPACyAUehlZpj2Vqlelmmka6UMaxdPwAasImsZBNfczh5Y0DDBZc/CDgqK7VnPvrf2XGujcAWNM8nZ9+6qt4Rx9DSypcnb5mmzqrOzO4nsLUNMK2gYZNwfXx/ICiF1ByA3wVYOiVsNecdDB0nWfX9bFy8yDv9hVIhAy6cmWSIbO6MW/JDbANA0PXKqN2hiJXdqmJOtU+C1k6R0yvoeiqYeF23qTUsLDwftbJ1ERtDmur4eE3uuhIF6iJOFhmpQDGQL6EYxrUxULVQg5bbDvaMZCHsK2jA93Z0tB0MoNEuFIsI1/2ebNjkEVTU9Uw15gIMW9SgjfaKwUtIpZB2DJpiDmELIOVm9LUxxwa4s6Ioy7JsM28SSamDpNq6jh+TqUq4P4WGqRghBBCCDGxSJAap3a0VmKw4OH6Aa4XsClbxtCgIeZQ9hX9+TK2bjCjPkJ93GHl5jSZgkciZDJQ8GiIh6iL2jhDa5TCtkGo5NFWG2Zjf4HubJnJNUZlXyKvUkzAd31eb8+SK7nkSj7pYplAVar02VqApukoBZZp0JKw0XSDkhtU1mNFbFxfMbsxzvSGCH5Q+XDv+QFTvRyXXfdlbLdEIRzjrv/v/3H/0Weg2TantiaZXPPnkZeQZeBYOsmIRbboEXesoc2BLXRNQ0cxWHAxhoppWLpBTcTCNnSm1kbJFl3SRZdU2CLhmBTcyrQ1NB3LUtSELUquT9H1yJcVIcukMe6gVKVqXl3UYW5zgpqoXd27qej6hCwDy6hU6BtpxGZbO1sno2kaR8+qpy9XZlVnlv6Ci6ZVytYbhsnRs+qIhyzW9+Z3OtpRdH36cmUa4g6zIza+qpR2Dw29Zme6yIa+fHVtHVTCWMdgiZakw4LJSbxAVUvfA3+ekji3YaejLs2pMMfMrqc25uyRn4HxSApGCCGEEBOHBKkJpvJB2SUZscmVfcp+QLrooqERtysbyfbky8xuilMbsZicClN0AwKl0RCzCdAYLHqkhqZ55V2f5kSIRMjDNjW6sqVKkQU/oFB26UqXsAyDWMjANjR03cD3fEpegK5ByNIwdQ3b1DFMk2m1EQ5vq2XB5BSxkMmSt7ppSlSmC3YOVj7AxxyDbLSWh045l0RvJ0/+v2+wKgjjqMoUNNvUhn3wLLo+qbDNoVNtlr3TBygcQycdKEpBgKEpdEOnLuYQsU3CVqU0d3MyTCJkDu2jFCJXconYJvMmRfECRaHs0ZUusXxdHxt7cwzkXVpSYVpTIXRNoytTxPcDDp1aUx1hcf2AN9ozI1bk29GIDezeOpnGRIiPL2xl2qZB3u7Kki/7RGyDWY0x5k9KAuxytKPo+mRLPrURi7C9/Y9/xDboGwqDW2yZktiUCI8YALeekiijLrsmBSOEEEKIiUGC1ART+aDsYRs6AZWKcXnXx/UCFFTWKWVKGGhkSh5Hz4yhAujNlejMlAlbOqmITUPcYbBQRtM08mWPeNjigMY4PbnS0Ia5LoMFH13TSIRNil5A0aus0/F8g0zRxQ8qm/LGQxbT62PUxmw+e8QUDptWWy0zvnmgyJruDK0rX+TQf/kWq87+Km9PPZCQGfDff/n/SEUd2mrDxHrzbOjPkwybQyXVK7YOIAe3xil5Aas6s2hAyfPpGCxR8ANSUYfWZIiCG2DoOlHboq22Uu486lTKf+fKlc1sZzbGqIlY1ERsmpMhyn5AcyKErlWqEOaKPuCh6xqHTK3hI7Mra4S60kUeX9W904p873edTGMixMfiDodOrRlxRGNXox0hyyDmmOTKHomQNWytFUqRK3vEHHPY9MPRTkmUURchhBBC7A8kSE0wIcvA0GDdQJ5s0SdQlT2cAgWeHxAoBcC7/QWKns/Ta3ppToQwdK263kejcmzENonYBmt78hzUkqA1FaI1FSJX8unLl1jbnUN3dEp+gO8HmIaGYxqETAPQ0DVFfcwh5Bh8ZFYdaDC59s8jMZqmscDMM+O7f8e0B+8G4LP3/5TvXHw9mwYLpMI29VG7Ukpd14hYBqZe2ZfID9R2AaQh7nDM7AaSYYt3+wvMbIgNVc4rU/YC+vMuPdkS0+tiTK0NV8uDa5pGW22EwbzL5sECg4Uy4a0CzqymOGcumkT7QJG3uzN/HglqiDN/cpLGRAilFCs3pXdYkW9tT5Zn1vRxxPQaDpmSYmNfns2Dhfc0YrOzEY1djXaELIOptRE29uWrJdqra60KZWzTYEpNZFho2tmURKUUvdkSxbJPoexVpzHKqIsQQgghJjoJUhOMY+oYul6plq0UxbKPOVRtTtfA9cE0NBSVSnv9uTK5kkdT3KYmbOEpRU+2RHu6yEEtCaK2SW3EQtMYCloGmgb9+TJNQ6M0r7WniVjGUIW8SsgJWTq2qRMLWXiBGmpbZd8ppRQDg3mMH91M3Xf/ifpMBqVpvP3xc7jrjItw0KmLRqsb5npKMb0uSlPcIRYy8X3FuwP5YQEE4LE3u9k0kKfkVTb+tQwD09CYWhshHrIpegEvbexHAzb0FUiErWqYSkVsZjXGCJSi6AbV68+ojzGlNkIibNGcCLFoaoqyr7YbZdlZRb7BgktPtswrm9Ks78uRili0psIcNb2ORNjaqyM2qYjF3OYEJS8gCBR9+TJeycXUdZoTYXRdY25zYtj0wh2V7h7Il1nfm+etrgw1EZulq3tY25OXKXxCCCGE2C9IkJqAIrZBbcyh6FWm9OVKlTClDe3nZOga8ZCB74MyKpX7XKURs0wMHbqylWlaBddnweQUJ89rpmOwNGzNy+yGBGHLJAh8NvYXKZQ9AqVwPUU8ZOJYlcDkBZXQMVAoc2hDHWXP58Vf/ZGZV32TxNq3AEjPPwT3xpuoP+pDnDWQ57m1/ZQ9j3jIruxxFFQ2G55SF+HY2fXYpjFsylh3prTNlLoQhbLHsnd6yZZ8jp9TTyrioFAM5F02D+TIlV3W9+VJhq3qNMOS53P8nMZqWEoXXDb251m+tne79U7bjrbsaPrbQL7Myk1psiUXy9BoiDuELYN3urP0ZsscN1TBbm/Zugz3QKFMy9B6r0ApsqXK2rhtpxeOVLq75FZCaVemRGPCYdGUFI5p7HJjYSGEEEKIiUKC1ART9hX1cZui69ObKzGpxqR9sIgfBORdH8vQidgmybBFT9alJeWQKfqVUaZCmen1EdrqYiQjJpahs2hqitqYw4Etatial2TY5PFVPby6aYA5TTE60pUwVXADNCojYZZhYOqVfY7qYiGakw5L3uqh+ZVXSa19i3KqhtcvuZznTjiLZDTEcX7AvEkpGuOharGCQrlSyW7WDvYd2tGUOtAwdB1T91nfW8DQdbxAUR+zGMxbDBTKdAwUmFYXxdS16hTB+ZOT1MYcutJFVmwc2Ol6p63bMtL0N6UU6/vy5FyPVNii4FWC1u5twPvB2bYgRMEf6uOdTC/c+px3+3O83p5mIO9yUEuCafURkuFKGBzL9yWEEEIIsTdJkJpgHFOnLuoQMg26syXyZY+wbaIChWHoJEIWpl4JGQAh0yQUM5lUEyZT9FgwKUVrTZgggHcH8pT9yrS8kda8zJuUoDtTZPPAlmlyFt2ZMr3ZEkpBbczAMQ0OaQpznN/Di5st2gcLpD53Pm97ed795OfwUjVMU2rYh+9dFStQ6s+hrlD22NS//ZQ6Nwjwg4CIbfDqpgHaBwsYemUT3ZCpUxtx6EgX2NCXoz7mDFujtKv1TiMFhZGmv+VKPr25Mo6p0ZEp0ZIMEbF0sqVKifqoY7Kpf+QNeD9o76UgxJZz1vVESBc9ZjXoJMI2hq6jUGhou9xYWAghhBBiopAgNcFs+UC/uivN7IYY6/tyhEyDXKmyD5IXKMK2iR9A2NIouD4N8RARq7I/VDJio6FRdL0R9zQKgoD1vXkyJY+4Y3LsAfVYhs6Tq7tJF11aUiEObkmQitgESjHz5WV87B//CXJ5/nTNHTjJGF6gSJ/7xWHFHrb98L2jYgVd6SKvblX+2w8CurMlDptaM6wQgqXruH6lol+25NKaCpMK25T9gMGii6HBrMY4p81rpTHh7NZ6J4UiV/YrGwB3ZThkSrK6J9JI09+6s0XWdmcpuj6moRMEAR2DRSxDxzI1dE1DKVjUVtn8duuAuDfWTb2XghCappEre7zbX0DTIFBZTL0S3tvqwiTD9k43FhZCCCGEmCgkSI1zI334bk46PLm6xNqeLINFj7Ln4/mKfMlD0zQSIYVjaSgsAhQNUYvBoktLMkLUMXa4p9Hrmwf539c6WNOdo+h66JrOlJowJx7YyBePncnKzWl6siVMXSPV086H/v1fmPToHwDIxGuIrnmLjTMOpH2wsunrR2bVUzMUprZ8+C66Pv258ohhoitd5P6XN7OqMztUfTAgU/TZ1F8gU/D42NwGUpFKsInYOmW/UqmvNmIRsU10XSOkGziGxpudWZoSIWY3RdH14WFxpPVOg4Uy63sL9OZKlH2fbMGjPupwzAH11alwW09/e7MjzYqNAwwUXFoSIeqjDh2ZIgP5IqmIxYz6KIFSdGZKPLe2H13TquvQtl2PtS+tNepKF3lubX+l2EjcIRaycb2A9sE86YLL/MkJTF3f4cbCQgghhBAThQSpcawrXayuc9ny4TvmmPRmy8RDJgc0xelOl2lP5+nJlYcKTujk3YDaqMHMhhgDeZeNA0UaEw6Ta0LkSyPvafT65kF+vnQtfXmXmoiF7+v05cqs7cny4oZ+zjhkEqfOa8b2Pcwbrif2g++hFQoEms5Tp5zD/37yi6xKQ7ngUvYCOtNFCiWPkw5uIhVxKLo+JTfgubX9DBbLI25m+9TbPby4YYCwVSmz3p8PyBRcsiWXF9b34fo+Jx3cQk3EJl8OsHQNWweFBqihYhiVMt+NCQfHNBgseNuNymy73mmwUObVd9Pkyi6psF0JCENTHx9f1T1svVRjIsRxMZts0SNfdmmrjTCQL5NzPXylmJwKMVh06c6WiDgGBzTG6c+VuOv5d2lJhWjajfVYY2XLlEfX9zmgMU5HpkgccCyDJjNEZ6bIup488bDJrJ1sLCyEEEIIMRFIkBqnRtr8teB6PL2mj3zJ5dgDGklFLHIln5Lnsam/wMrNAwSBTixkUPYVBdejtSaEZeg4pkGm5FHygu32NAqCgP99rYO+vMuUVIgN/QVKXkAyYlMTsdjQX+ThNzuJFzOc8YVPYL6zBoB1Bx3KnX/9dxQPPJjNvXkCCmiaRl3UZrDo8XZ3jpp1/RwxrYaN/UXSBRdTh6ZEeLswccjkJC+s78fUwTJ11vXmcf3KGqjWRIj1fQVe25Qm5lgsnlFPwfXIlDxmNsVJhi0KbkCm5GHqOi3JCJNrQtX3u62t1zu12RHW9xbIlV2a4iEUlaqGzakwBzbHWd+X32691GDBY7BYZnZjAtcPeH59P5u7cyTDFkrTsA2DzQNF5jbHmVobZnVXlvbBAgsmJ6vTE8e6IMVItkx5bEqEqY0GZIoeXdkSyZCFbeiELYPVXRk+NKNulxsLCyGEEEKMdxKkxqGdVaozdTAMnQ39eZLhBIOFMq+3p1ndlaXkVtb3zGxM0RAPky/71EQsTp3XhGOZO1ybs743z5ruHK1Jh+5cZRrhlrLhAE0Jm0zR4+2yRd+sA2nI52j/9j/yvfhCWmrCdGfKlDyf1kSY7myJghsQMnUKZZ/OdJFn1/UTtU3iIZPp9bERizs8s7aXTQN5DE1jTXeOwYILVDZ/dUydsG2QKbq825fnRauPaXURmhMh5jTHaE6GyZV8XD/AMnSijkG+5Fff77a2Xu/0ZnuGzYMFkiGLoldZXxW1TNpqI+i6PmJhhZIXUPJ8vMBEAZNSId7tzxEoRaboomsaMcdgVmMMyzDIFn3CtlHdb2vrduxLhRu2nvIYdUzmTUqwvi9PX65MpuhiaJV75/BptfvECJoQQgghxAdJgtQ4tKNiCK4f4CtFXdRmU3+BnnSRVZ1ZujJFNDTqY5Xw05kp4wca8ybF6c+7vN6e5fi5DTscQaiM3PgYuk226BJ1TDRNwyoXOeEPv2LpMR+n34gTcUyWXHoVJx46jQwOPLKKkheQKbpEbQvL1GmIawzky6QLLnnXR6nKiFfMMZnZENuuDVvCxIqN/XQMlgiZGsWyj1KKIAAI0BSE7crmu8mIRU3U4aSDmlnbm+ed7iwoiG1ViGJHa8C2tmW905Ore3ijI42mwLZ0mhMh2moj1UIZIxVWSBdc1vXkWeVm0HUNP1AYmkZ9zCYetvG3/D3FHFw/oOB6hGwDy9g+1O1LhRu2nfKYitgkw5VRTzcIcL0ALwiYlAqPdVOFEEIIIT5wEqTGoR1t/moZOqauUyh7rOnKkHN9Sm5lJEbXKwEqZGnMaY6RK7ts6CsyoyFSHfFIRawRq8bFHRPHNMiWXHylMDSY/8IS/upXP6C+exP1777D9eddSTJkkrZrKUViNBgadVGHrkwRLwiI2sNvtaJbeY2YY1Y3/932/UAl9LhBwOaBIopKeCp6PgoI2zoaGgXPI11S1IQswpZJf65E2DaZPylJb7ZcraK3ZbrgSGvARtKYCHH8nAZ6skVCpkEybBN1jGHnFF1/WGGFrnSRlzb2U/YVZT9gUixM2Q9oTxdZ359nnmNS9PxqYY9sUZEfqpwYdbZ//9tefyyNVOJd0zRiIRM1VMJ+Z+FUCCGEEGIikSA1Do20+StA1DGojdgsfbu7UlxCh6htEihvKGC5FNDpSpeYXhelN1eirS5C2Q/YNJDnpQ3lEavGtdVFmNkQ5aUNAzR3beS8O29k/itPA9Bf28gTcz9McyJEImSSKfnVEHbo1Br+uLKdfMnH1n3QKkEjV/IwTY3p9VFqYxabBip7UXWmi7RuNZoxkC+zvi/P+t4cG/vzhCyDvmyJXMkj6pgoNAJAKSj7AfWJEKmoSX/eo+j6TG+IDdt4tidXwjb07daA7UxN1GZ2Y4I13ZntQtSWka0Z9TGUUrQPFCrFMvJljpyWYuWmDN25Eqmwzaz6GC9vGuCldwc5sCVeLezRnam857BtgAK2ynW7M3K2N41U4n204VQIIYQQYqKQIDUOjTQyAKChURsx6c6U8AKFbRjYpoZWAtf3CVsmhqHTOVhiam0ELwjIltxqtTzX94k5ViV8BYq3u9LVqnGnTI8z/5bvc+z9/4XluXiGyf+e/Fl+cfy5WMk4J0xJ0p0tVz/0a5rGR2bX05srseStbnqyxUoJdreyHqg+6tBWEyVbdjmoOUFXtsRrmwdpTjjous5AvszKTZVKeUU3oDZqMTkV4eWyT3/eJV/yUEPBQ9c04o5BU8IhX/aJOWZ1dGs0m/uOtD5sV+FB0zTSRZffv9rOQN5lVWeG1mSI2qjD/MmJasl0LwiYnAqTLXs0xP5c6GJWY4KPzHZYuSk9LsLJ1iXe32s4FUIIIYSYCCRIjUOapnFwa5z1vTle3ZSmOelQG7XpzhRZtrafkhvgmBrpokfJq+wdpes6yYgFGmSLPumCi2MZDORdAqUolj0UGuv7Cnh+gGno1EZssqWhqnR3/gcH3nMrAC/OPZIfnnkJg5On05IKM6M+ykDBpzZqcXBrvPqhvzER4oxDJlEXtfnDqx2805OjLmrRnIiQitpkyy5Rx2J6Q5SmpMOL6wd4oyNDW22EdT15+gtlLB3qojYxxyAesjhkSoqc61Ms+2gahC2DiG1gmzolNyDimEypiQybJqhp2rBpi1umMXZnStuVjx9p76YdhYe6qEN/vkxPtkRjvFL98J2eLH25SgicNynB/MmJaqELXdPoz5U4ZnYDyYg9LLjVx5xxE052FU6FEEIIIfYHEqTGoa50kdc2Zyi4Hl2ZAu/0ZNE1RW+2zGDeRdMUjmngawFlVxEP6YQsAzdQ6ABK0Zcr05AIkwiZdGdL9OdcPKUqpaxDFmU/oHMwj2EYvNkxyKIv/g01f/oTwTe+QXDIR/nopjTtg0XSRZd1vXlijkHI0nhtc6ZSIGKrfZXOOnQyzckwdzy7AdPQhvazgpZkhLa6MMmwTcyxmFbvMjkVoTNTZE1Phqhj0ZoMM7U2zIa+Au2DeRrjDnObE2zsy9MQt3GHNhqOOiZTaiMYus7c5sSwqXA7229LoYaNAu1o76Ztw4NtaLy0cYDeXKlaOVEBEdskbOoMljzW9+VZEE5WC13kSh6OZdCYCG1XgW+8hRNN08a8iqAQQgghxFiSIDXObL1/1KRUhBn1Mdb2ZvnflV10DOaJD5Ulz7sBsZBJNvBIFz1s0yBqG3SniwRURnIWz6hlVmOMXy9fj+crmhKVKoBWPstx/30LTatX8m9X3MKGvjzF0GR4+mk0pZiZd3Fskyfe6iZk67QkwtRGbUpeMGIQ0TSNA1sSHDGtFsPQsAy9WoZcG1oUVHR9aqM2x89toDtTIl8OmFobIRE20agEr3TBpStTojZq05crEbJMIja01oSZ3RBH1yG1zVS43dlva3f3bto6PPTnymweKAyrnBh1DOqiDu2DeZIhi75cmVzJrxZj2NV6JwknQgghhBDjhwSpcWSk/aMG8mXW9eZJF8qYhk7cMbEMnc50kUzBJRG2yJRcBvJlDK0SFhZNreH8D0/jgOY463pyZEs+tRELDZjz+O857qffI97bBcD8N57juQMOr6zZGRrZebc/x+vtaQbyLrMb4ziWjmlUvnYURFIRi0k1Q+u6hu19NbyoQk3UHprqZmPqWjVoJcN2dc3R5sEC8ZBJMmTiWAYNcYeoY2w3LW9399vaeuRnd/duGqlyooZGW12YdMGlv1CuVhjUSuyT652EEEIIIcR7J0FqHNl2/yilFOv78vTnXBSQjFiUvID6mEMQKPpyLvmyT8QyyJd9GhIhDm+r5VOHT6YpWamOF7IMYo5JbM0qPvXLf2Xqy8srr9UyhUcvuoJXDjyCmGGQLXk8t66fwUKZqG2io9MYd+hIF8gUPeZPTpAM2zsMIqOp+LajYhrJsM28SSamDotn1HHcAfVomkbZVyNOhdud/ba2HjXaYnf2btpR5cQtgW9VR5bNgwW6s0VSYXufXe8khBBCCCHeGwlS48i2oyC5kk9vtoxjaPhKgYJAKSxDZ1JNhJBVZLDgEbYMTF3nY3MaOXV+y7ARm1I6y9m/up5D7/sVRuDj2g7PnPMFnjrzAnp9DdvQmVITZm13vjqyM1Bw8VVAynFIhCw6M0XW9xaYP9lCQ9thENndim+7Cl3NqTDHzK6nLr7zULKr/bY0wPMD3GB4O3dn76YdhT2ARMiiPl4JhkdMqyVkGfv0eichhBBCCDF6EqTGkW1HQXpzJTb05/C8gLIb0FkqErFNXD8gHrJoToZxzDKOpdMUD/GXC1qq4aNagKF7kBNWLMMIfFYcfjz3//XXybZOxtR0mhM2uq4xuSbCQL5UHdmx9Mo0vi0hJRW26c2VKiM7jrnTILK7RRX2RJntne23VRd1WN+bJepYWPqf27m7ezftKuylIjYfmlEnI1BCCCGEEBOUBKlxZOtRkJRns7ozS67kkwyZNCZsNvYVKboePdkSpq6hFJR8n0TY5sOz6qmNOQD0LnueJ/wk/YFOYzzC6//wff64dhNPzTycZNjiwIYYUcckW/JIRWxmNMRYvra3OrITdQxqozYd6SKOqWOZOl7JxfWDPVpU4f1WstvZfltTa0Os6c7iBwGg8AM16r2bZE8lIYQQQoj9lwSpcWTLKEh3psjz6/souD7NCYe+nIttGtTHbZSqlNnePJjHMgySYYujptdw9Kx6tMFB1Le/Te0ttzDt/EtJfu2ySlj40FEkFpQ5qDfPW10Z1nTnOLglwayhQGAZw0d2NE2jrTZCpuDRlS0RNnV0TcP1A9b15vZoUYX3U8luZ6NG/XmXQ6emqInYZEoevfnyewpB461suRBCCCGE2DMkSI0zjYkQi6bW8OqmQXz158dDlsHU2ghFL6BjoMBA0WNyTYhT5zVzzMw6Gn/733D55Wjd3ZXrdG9iYKvrpiI2ybBFSzJEf77MR2bXM60+Wi1qse3IzpYy4+uHwldNxMb31T43GrOrUaOGPRCCpGy5EEIIIcT+R4LUOJQIW0yvj5KK2ARKkS/79GRL9OXKmEbAtPoYXhDw6SOmsnhgHdopn4TllWp83py5/OmLf49x4l9gbBMYNE2jLuZQ8HzCtjmsJPhIIzuWoRMPGXxoRh1HTKthUiqyT47G7GrUSEKQEEIIIYQYLQlS45Bj6timjqlrRB2Lmgi0JkPkSj5uEOB6AV4QsPAPd6J95W9AKYjF4OqryXz+i/S+0U1imwIMW+yoUMSORnZmNSb2qRGoHZFRIyGEEEIIsSdJkBqHRiqioGkasZCJUop1vTlmNsSJnHYyXObA2WfDdddBayupEabpbbGrQhGyHkgIIYQQQogKCVLj0I6m2oVfeJbQ00+SvPDSSrGHRCOsXg2TJ+/y3N2tWCcjO0IIIYQQQkiQGre2nmrXs3Yjc3/0Peb8/i4AZn3qL6lNTKkcuFWIGulcKdsthBBCCCHE6EmQGscaIybHP3wn6sor0QcGAFDnn0/tvLm7Plem6QkhhBBCCPGeSZAar5YuhUsuQXv5ZTSARYvg3/8d7cMf3u1LyDQ9IYQQQggh3hsJUuNRoVApINHVBakU/PM/w0UXgWGMdcuEEEIIIYTYL0iQGi88rxKUNA3CYfje9+Cpp+Bf/gUaGsa6dUIIIYQQQuxX9F0fIsbc44/DIYfAXXf9+bHzz4ef/lRClBBCCCGEEGNAgtS+bNMm+Mxn4Pjj4bXX4NprK5vrCiGEEEIIIcbUhAlSN998M9OmTSMUCnHUUUfx7LPPjnWT3rtyubKB7pw5cMcdlel8X/oSPPJI5c9CCCGEEEKIMTUhgtSdd97J1772Na666ipefPFFFi5cyMknn0xXV9dYN230li6FhQvhsssgl4PFi+H55+FHP4La2rFunRBCCCGEEIIJEqSuv/56LrzwQi644AIOOuggfvzjHxOJRPjP//zPsW7a6JXL8Oab0NgIt91WCVaHHjrWrRJCCCGEEEJsZdxX7SuXy7zwwgtcccUV1cd0XefEE09k2bJlI55TKpUolUrV79Pp9Afezt32sY/Bz38OZ55ZKW0uhBBCCCGE2OeM+xGpnp4efN+nqalp2ONNTU10dHSMeM61115LMpmsfk2ZMmVvNHX3nX++hCghhBBCCCH2YeM+SL0XV1xxBYODg9WvjRs3jnWThBBCCCGEEOPIuJ/aV19fj2EYdHZ2Dnu8s7OT5ubmEc9xHAfHcfZG84QQQgghhBAT0LgfkbJtm8MOO4xHHnmk+lgQBDzyyCMsXrx4DFsmhBBCCCGEmKjG/YgUwNe+9jXOO+88Dj/8cI488khuvPFGcrkcF1xwwVg3TQghhBBCCDEBTYggdc4559Dd3c2VV15JR0cHhxxyCH/84x+3K0AhhBBCCCGEEHuCppRSY92IsZZOp0kmkwwODpJIJMa6OUIIIYQQQogxsrvZYNyvkRJCCCGEEEKIvU2ClBBCCCGEEEKMkgQpIYQQQgghhBglCVJCCCGEEEIIMUoSpIQQQgghhBBilCRICSGEEEIIIcQoSZASQgghhBBCiFGSICWEEEIIIYQQoyRBSgghhBBCCCFGSYKUEEIIIYQQQoySBCkhhBBCCCGEGCUJUkIIIYQQQggxSuZYN2BfoJQCIJ1Oj3FLhBBCCCGEEGNpSybYkhF2RIIUkMlkAJgyZcoYt0QIIYQQQgixL8hkMiSTyR0+r6ldRa39QBAEbN68mXg8jqZpY9KGdDrNlClT2LhxI4lEYkzasL+RPt/7pM/HhvT73id9vvdJn+990ud7n/T53qGUIpPJ0Nraiq7veCWUjEgBuq4zefLksW4GAIlEQn4w9jLp871P+nxsSL/vfdLne5/0+d4nfb73SZ9/8HY2ErWFFJsQQgghhBBCiFGSICWEEEIIIYQQoyRBah/hOA5XXXUVjuOMdVP2G9Lne5/0+diQft/7pM/3PunzvU/6fO+TPt+3SLEJIYQQQgghhBglGZESQgghhBBCiFGSICWEEEIIIYQQoyRBSgghhBBCCCFGSYKUEEIIIYQQQoySBKl9xM0338y0adMIhUIcddRRPPvss2PdpAnj6quvRtO0YV9z586tPl8sFrn44oupq6sjFotx9tln09nZOYYtHn+eeOIJPv7xj9Pa2oqmafzud78b9rxSiiuvvJKWlhbC4TAnnngiq1evHnZMX18f5557LolEglQqxec//3my2exefBfjy676/Pzzz9/uvj/llFOGHSN9PjrXXnstRxxxBPF4nMbGRs4880xWrVo17Jjd+X2yYcMGTj/9dCKRCI2NjXzzm9/E87y9+VbGjd3p8+OOO267e/2LX/zisGOkz3ffLbfcwoIFC6obvi5evJgHH3yw+rzc43vervpc7vF9lwSpfcCdd97J1772Na666ipefPFFFi5cyMknn0xXV9dYN23COPjgg2lvb69+LV26tPrcV7/6Ve6//37uuusulixZwubNm/nEJz4xhq0df3K5HAsXLuTmm28e8fnrrruOH/7wh/z4xz9m+fLlRKNRTj75ZIrFYvWYc889l9dee42HHnqIBx54gCeeeIIvfOELe+stjDu76nOAU045Zdh9f/vttw97Xvp8dJYsWcLFF1/MM888w0MPPYTrupx00knkcrnqMbv6feL7Pqeffjrlcpmnn36aX/ziF9x2221ceeWVY/GW9nm70+cAF1544bB7/brrrqs+J30+OpMnT+a73/0uL7zwAs8//zwf+9jHOOOMM3jttdcAucc/CLvqc5B7fJ+lxJg78sgj1cUXX1z93vd91draqq699toxbNXEcdVVV6mFCxeO+NzAwICyLEvddddd1cfeeOMNBahly5btpRZOLIC65557qt8HQaCam5vV97///epjAwMDynEcdfvttyullHr99dcVoJ577rnqMQ8++KDSNE1t2rRpr7V9vNq2z5VS6rzzzlNnnHHGDs+RPn//urq6FKCWLFmilNq93yd/+MMflK7rqqOjo3rMLbfcohKJhCqVSnv3DYxD2/a5Ukode+yx6m//9m93eI70+ftXU1Ojfvazn8k9vhdt6XOl5B7fl8mI1Bgrl8u88MILnHjiidXHdF3nxBNPZNmyZWPYsoll9erVtLa2MmPGDM4991w2bNgAwAsvvIDrusP6f+7cuUydOlX6fw9Zu3YtHR0dw/o4mUxy1FFHVft42bJlpFIpDj/88OoxJ554Irqus3z58r3e5oni8ccfp7GxkTlz5vClL32J3t7e6nPS5+/f4OAgALW1tcDu/T5ZtmwZ8+fPp6mpqXrMySefTDqdHvZ/n8XItu3zLX79619TX1/PvHnzuOKKK8jn89XnpM/fO9/3ueOOO8jlcixevFju8b1g2z7fQu7xfZM51g3Y3/X09OD7/rCbH6CpqYk333xzjFo1sRx11FHcdtttzJkzh/b2dr7zne9wzDHHsHLlSjo6OrBtm1QqNeycpqYmOjo6xqbBE8yWfhzpHt/yXEdHB42NjcOeN02T2tpa+Xt4j0455RQ+8YlPMH36dNasWcPf//3fc+qpp7Js2TIMw5A+f5+CIOArX/kKRx99NPPmzQPYrd8nHR0dI/4sbHlO7NhIfQ7w2c9+lra2NlpbW3nllVe47LLLWLVqFXfffTcgff5evPrqqyxevJhisUgsFuOee+7hoIMOYsWKFXKPf0B21Ocg9/i+TIKUmPBOPfXU6p8XLFjAUUcdRVtbG7/5zW8Ih8Nj2DIhPjif/vSnq3+eP38+CxYsYObMmTz++OOccMIJY9iyieHiiy9m5cqVw9Zbig/Wjvp863V98+fPp6WlhRNOOIE1a9Ywc+bMvd3MCWHOnDmsWLGCwcFBfvvb33LeeeexZMmSsW7WhLajPj/ooIPkHt+HydS+MVZfX49hGNtVvOns7KS5uXmMWjWxpVIpDjjgAN5++22am5spl8sMDAwMO0b6f8/Z0o87u8ebm5u3K67ieR59fX3y97CHzJgxg/r6et5++21A+vz9uOSSS3jggQd47LHHmDx5cvXx3fl90tzcPOLPwpbnxMh21OcjOeqoowCG3evS56Nj2zazZs3isMMO49prr2XhwoXcdNNNco9/gHbU5yORe3zfIUFqjNm2zWGHHcYjjzxSfSwIAh555JFhc2PFnpPNZlmzZg0tLS0cdthhWJY1rP9XrVrFhg0bpP/3kOnTp9Pc3Dysj9PpNMuXL6/28eLFixkYGOCFF16oHvPoo48SBEH1Hwzx/rz77rv09vbS0tICSJ+/F0opLrnkEu655x4effRRpk+fPuz53fl9snjxYl599dVhIfahhx4ikUhUp/GIP9tVn49kxYoVAMPudenz9ycIAkqlktzje9GWPh+J3OP7kLGudiGUuuOOO5TjOOq2225Tr7/+uvrCF76gUqnUsOor4r37+te/rh5//HG1du1a9dRTT6kTTzxR1dfXq66uLqWUUl/84hfV1KlT1aOPPqqef/55tXjxYrV48eIxbvX4kslk1EsvvaReeuklBajrr79evfTSS2r9+vVKKaW++93vqlQqpe699171yiuvqDPOOENNnz5dFQqF6jVOOeUUtWjRIrV8+XK1dOlSNXv2bPWZz3xmrN7SPm9nfZ7JZNQ3vvENtWzZMrV27Vr18MMPq0MPPVTNnj1bFYvF6jWkz0fnS1/6kkomk+rxxx9X7e3t1a98Pl89Zle/TzzPU/PmzVMnnXSSWrFihfrjH/+oGhoa1BVXXDEWb2mft6s+f/vtt9U111yjnn/+ebV27Vp17733qhkzZqiPfvSj1WtIn4/O5ZdfrpYsWaLWrl2rXnnlFXX55ZcrTdPUn/70J6WU3OMfhJ31udzj+zYJUvuIf/u3f1NTp05Vtm2rI488Uj3zzDNj3aQJ45xzzlEtLS3Ktm01adIkdc4556i33367+nyhUFBf/vKXVU1NjYpEIuqss85S7e3tY9ji8eexxx5TwHZf5513nlKqUgL9H/7hH1RTU5NyHEedcMIJatWqVcOu0dvbqz7zmc+oWCymEomEuuCCC1QmkxmDdzM+7KzP8/m8Oumkk1RDQ4OyLEu1tbWpCy+8cLv/OSN9Pjoj9Tegfv7zn1eP2Z3fJ+vWrVOnnnqqCofDqr6+Xn39619Xruvu5XczPuyqzzds2KA++tGPqtraWuU4jpo1a5b65je/qQYHB4ddR/p89/3f//t/VVtbm7JtWzU0NKgTTjihGqKUknv8g7CzPpd7fN+mKaXU3hv/EkIIIYQQQojxT9ZICSGEEEIIIcQoSZASQgghhBBCiFGSICWEEEIIIYQQoyRBSgghhBBCCCFGSYKUEEIIIYQQQoySBCkhhBBCCCGEGCUJUkIIIYQQQggxShKkhBBCCCGEEGKUJEgJIYTY52iaxu9+97uxbsZOffSjH+W///u/x7oZO3Tcccfxla985T2f/6EPfYj/+Z//2XMNEkKICUaClBBC7MeWLVuGYRicfvrpoz532rRp3HjjjXu+UaPwftr/ftx33310dnby6U9/eq++7kgef/xxNE1jYGBgj17329/+NpdffjlBEOzR6wohxEQhQUoIIfZjt956K3/zN3/DE088webNm8e6OaM2Vu3/4Q9/yAUXXICuT9x/Rk899VQymQwPPvjgWDdFCCH2SRP3XwAhhBA7lc1mufPOO/nSl77E6aefzm233bbdMffffz9HHHEEoVCI+vp6zjrrLKAybWz9+vV89atfRdM0NE0D4Oqrr+aQQw4Zdo0bb7yRadOmVb9/7rnn+Iu/+Avq6+tJJpMce+yxvPjiix9I+++77z5mz55NKBTi+OOP5xe/+MV2ozdLly7lmGOOIRwOM2XKFC699FJyudwOX7e7u5tHH32Uj3/848Me1zSNn/zkJ/zlX/4lkUiEAw88kGXLlvH2229z3HHHEY1G+fCHP8yaNWuGnXfLLbcwc+ZMbNtmzpw5/PKXv9zuuj/72c8466yziEQizJ49m/vuuw+AdevWcfzxxwNQU1ODpmmcf/751XODIODv/u7vqK2tpbm5mauvvrr6nFKKq6++mqlTp+I4Dq2trVx66aXV5w3D4LTTTuOOO+7YYV8IIcT+TIKUEELsp37zm98wd+5c5syZw//5P/+H//zP/0QpVX3+97//PWeddRannXYaL730Eo888ghHHnkkAHfffTeTJ0/mmmuuob29nfb29t1+3Uwmw3nnncfSpUt55plnmD17NqeddhqZTGaPtn/t2rX81V/9FWeeeSYvv/wyF110Ed/61reGXWPNmjWccsopnH322bzyyivceeedLF26lEsuuWSHr7t06dJqUNrWP/7jP/LXf/3XrFixgrlz5/LZz36Wiy66iCuuuILnn38epdSwa99zzz387d/+LV//+tdZuXIlF110ERdccAGPPfbYsOt+5zvf4VOf+hSvvPIKp512Gueeey59fX1MmTKluo5p1apVtLe3c9NNN1XP+8UvfkE0GmX58uVcd911XHPNNTz00EMA/M///A833HADP/nJT1i9ejW/+93vmD9//rDXPfLII3nyySd39VchhBD7JyWEEGK/9OEPf1jdeOONSimlXNdV9fX16rHHHqs+v3jxYnXuuefu8Py2tjZ1ww03DHvsqquuUgsXLhz22A033KDa2tp2eB3f91U8Hlf3339/9TFA3XPPPe+r/ZdddpmaN2/esHO+9a1vKUD19/crpZT6/Oc/r77whS8MO+bJJ59Uuq6rQqEw4uvecMMNasaMGds9Dqhvf/vb1e+XLVumAHXrrbdWH7v99ttVKBQa9h4uvPDCYdf55Cc/qU477bQdXjebzSpAPfjgg0oppR577LFh72mLY489Vn3kIx8Z9tgRRxyhLrvsMqWUUj/4wQ/UAQccoMrl8ojvUyml7r33XqXruvJ9f4fHCCHE/kpGpIQQYj+0atUqnn32WT7zmc8AYJom55xzDrfeemv1mBUrVnDCCSfs8dfu7OzkwgsvZPbs2SSTSRKJBNlslg0bNuzR9q9atYojjjhi2HlbRtS2ePnll7ntttuIxWLVr5NPPpkgCFi7du2Ir10oFAiFQiM+t2DBguqfm5qaAIaN8jQ1NVEsFkmn0wC88cYbHH300cOucfTRR/PGG2/s8LrRaJREIkFXV9eIbdjReQAtLS3V8z75yU9SKBSYMWMGF154Iffccw+e5w07PhwOEwQBpVJpl68lhBD7G3OsGyCEEGLvu/XWW/E8j9bW1upjSikcx+Hf//3fSSaThMPhUV9X1/Vh0+sAXNcd9v15551Hb28vN910E21tbTiOw+LFiymXy3u0/bsjm81y0UUXDVsbtMXUqVNHPKe+vp7+/v4Rn7Msq/rnLevGRnpstJXwtr7GluvszjV2dt6UKVNYtWoVDz/8MA899BBf/vKX+f73v8+SJUuq5/X19RGNRt/TvSCEEBOdjEgJIcR+xvM8/uu//osf/OAHrFixovr18ssv09rayu233w5URjMeeeSRHV7Htm183x/2WENDAx0dHcPC1IoVK4Yd89RTT3HppZdy2mmncfDBB+M4Dj09PXu8/XPmzOH5558fdu5zzz037PtDDz2U119/nVmzZm33Zdv2iK+/aNEiOjo6dhimRuPAAw/kqaeeGvbYU089xUEHHbTb19jSzm3/LnZHOBzm4x//OD/84Q95/PHHWbZsGa+++mr1+ZUrV7Jo0aJRX1cIIfYHEqSEEGI/88ADD9Df38/nP/955s2bN+zr7LPPrk6Pu+qqq7j99tu56qqreOONN3j11Vf53ve+V73OtGnTeOKJJ9i0aVM1CB133HF0d3dz3XXXsWbNGm6++ebtymfPnj2bX/7yl7zxxhssX76cc889d1QjHrvb/osuuog333yTyy67jLfeeovf/OY31cp+W0aGLrvsMp5++mkuueQSVqxYwerVq7n33nt3Wmxi0aJF1NfXbxeA3otvfvOb3Hbbbdxyyy2sXr2a66+/nrvvvptvfOMbu32NtrY2NE3jgQceoLu7m2w2u1vn3Xbbbdx6662sXLmSd955h1/96leEw2Ha2tqqxzz55JOcdNJJo35fQgixP5AgJYQQ+5lbb72VE088ccTpb2effTbPP/88r7zyCscddxx33XUX9913H4cccggf+9jHePbZZ6vHXnPNNaxbt46ZM2fS0NAAVEZYfvSjH3HzzTezcOFCnn322e1Cwa233kp/fz+HHnoon/vc57j00ktpbGzc4+2fPn06v/3tb7n77rtZsGABt9xyS7Vqn+M4QGXUbcmSJbz11lscc8wxLFq0iCuvvHLYlMFtGYbBBRdcwK9//evdbvOOnHnmmdx0003867/+KwcffDA/+clP+PnPf85xxx2329eYNGkS3/nOd7j88stpamraaQjcWiqV4qc//SlHH300CxYs4OGHH+b++++nrq4OgE2bNvH0009zwQUXvJe3JoQQE56mtp3MLoQQQkxQ//zP/8yPf/xjNm7c+L6u09HRwcEHH8yLL744bARnIrnsssvo7+/nP/7jP8a6KUIIsU+SYhNCCCEmrB/96EccccQR1NXV8dRTT/H9739/t0dsdqa5uZlbb72VDRs2TNgg1djYyNe+9rWxboYQQuyzZERKCCHEhPXVr36VO++8k76+PqZOncrnPvc5rrjiCkxT/j+iEEKI90eClBBCCCGEEEKMkhSbEEIIIYQQQohRkiAlhBBCCCGEEKMkQUoIIYQQQgghRkmClBBCCCGEEEKMkgQpIYQQQgghhBglCVJCCCGEEEIIMUoSpIQQQgghhBBilCRICSGEEEIIIcQo/f+po3dtWdtKlgAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 120
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
